Method and System to Provide Personalized Pharmaceutical Compositions and Dosages

ABSTRACT

Provided is a method for assessing cardiovascular risk in Metabolic Syndrome and Type 2 Diabetes patients. The method involves obtaining data from a Type 2 diabetes patient or a Metabolic Syndrome patient and determining a Fayad/Schentag index which includes a Glucose Supply Index (S) to an Insulin Demand Index (D) ratio. The Fayad/Schentag index is used in scoring cardiovascular risks of Metabolic Syndrome patients and for recommending and implementing therapeutic interventions that can be shown to lower cardiovascular risk

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation in part of U.S. patent applicationSer. No. 12/911,497, filed on Oct. 25, 2010, which in turn claimspriority to U.S. Provisional application No. 61/254,373 filed on Oct.23, 2009, the disclosures of each of which are incorporated herein byreference.

FIELD OF THE INVENTION

The present invention in one aspect relates to management of Type 2diabetes and in another aspect relates to management of Metabolicsyndrome. In particular embodiments, the invention relates to modulatingdrug therapies to improve cardiovascular outcomes for Type 2 diabeticsand/or for individuals who have or are at risk for developing Metabolicsyndrome.

BACKGROUND OF THE INVENTION

The most prevalent form of diabetes is Type 2 diabetes. Type 2 diabetesaccounts for approximately 90-95% of all diagnosed cases of diabetes.Type 2 diabetes was previously known as non-insulin-dependent diabetesmellitus (NIDDM). Type 2 diabetes was also previously known asadult-onset diabetes. However, this form of diabetes is becomingincreasingly prevalent in the growing population of overweight andclinically obese children and adults. Type 2 diabetes typically beginswith insulin resistance, a disorder in which the body's cells do notrespond to insulin properly, followed by a gradual loss on part of thepancreas to produce and secrete insulin in at least some patients. Type2 diabetes is associated with a variety of factors including older age,obesity, family history of diabetes, history of gestational diabetes,impaired glucose metabolism, dietary intake of carbohydrates andglucose, low physical inactivity, and various races or ethnicities.Further conditions considered consequences of diabetes itself includehypertension and cardiovascular disease, especially atherosclerosis andvascular clotting and inflammation that may lead to ischemia of theheart tissues.

According to the American Diabetes Association, 20.6% of adults over theage of 60 have diabetes and 34.8% of all adults have either diabetes orpre-diabetes. A major goal of therapeutic treatment of diabetic patientsis to delay or prevent the complications associated with chronichyperglycemia. Cardiovascular complications are the most frequent causeof morbidity and mortality in diabetic patients. These complicationsinclude microangiopathy, retinopathy, neuropathy, nephropathy, andmacroangiopathy, which is an accelerated form of atherosclerosis. Mostpatients with Type 2 diabetes die from cardiovascular disease, and ithas only recently been demonstrated that some diabetes medicamentsaccelerate the development of cardiovascular disease in Type 2diabetics, while others may prevent or slow down the rate of injury.

Another condition that is widely prevalent and which has multiplerelationships and causal connections with cardiovascular disease isMetabolic Syndrome (MS). MS has many different manifestations such asType 2 Diabetes, hyperlipidemia, obesity and Non-alcoholic fatty liverdisease (NAFLD), but heretofore there has been no means of trackingprogression of MS in patient populations that may have any or all ofthese conditions to varying degrees. Thus, there is an ongoing need forimproved cardiovascular risk scoring processes for metabolic syndromemanifestation diseases, as well as for Type 2 diabetes. The presentinvention meets these and other needs.

SUMMARY OF THE INVENTION

The present invention comprises two main, related aspects. These ingeneral relate to assessing, scoring, treating, monitoring treatment,adjustments and changes in drug therapy, and pharmaceutical preparationsused for disorders which have in common with one another various effectson, and relationships with, the cardiovascular system. The first mainaspect relates to Type 2 diabetes and the second main aspect relates toMS. Those skilled in the art will recognize that characterizing theinvention by way of the term “aspects” is not intended to mean thatthese disorders are mutually exclusive. To the contrary, they havemultiple relationships with each other and certain overlappingetiologies, conditions, signs and symptoms as will be more fullydescribed herein.

In the first aspect, the invention provides a method for determining asuitable drug combination for the treatment of Type 2 diabetes byobtaining data from a Type 2 diabetes population in which all of theType 2 diabetics are not taking any Type-2 diabetes drugs and obtainingreference levels of glucose supply parameters that include: carbohydrateexposure (CE), hepatic glucose uptake (HGU), hepatic gluconeogenesis(GNG) and insulin resistance (IR). From the data the following insulindemand parameters are also determined: peripheral glucose uptake (PGU)and peripheral insulin exposure (PIE). Data from discrete samples ofType-2 diabetes populations in which all individuals are being treatedwith one or more Type 2 diabetes drugs at a therapeutic dose are alsoobtained, and the effects of the drugs on the glucose supply and insulindemand parameters are used to determine adjustment factors whichrepresent the effect of each of the drugs at the therapeutic dosage. Theadjustment factors are used to determine a Glucose Supply Index (S) foreach drug calculated as follows:

1+CE+HGU+GNG+IR,

and an Insulin Demand Index (D) calculated as follows:

1+PGU+PIE.

The Glucose Supply Index and the Insulin Demand Index form a ratio whichis indicative of the relationship between the effect on the glucosesupply and on the insulin demand parameters for the drugs, and it isconsidered that an SD ratio of above 1.0 is indicative that the drug ordrug combination for which the SD ratio is calculated functions on theglucose supply side of Type 2 diabetes management, while an SD ratio ofbelow 1.0 is indicative that the drug or drug combination for which theSD ratio is calculated functions on the insulin demand supply side ofType 2 diabetes management.

In another embodiment, the invention also provides a method fordetermining modulation of cardiovascular risk for a Type 2 diabetic whois being treated with at least one drug. This embodiment comprises a)obtaining one or more physiological parameters from the Type 2 diabeticat a first time point and determining the SD ratio for the drug withthat is being used to treat the Type 2 diabetic and b) assigning a firstcardiovascular risk score for the individual by summing values for oneor more physiological parameters that are presented in a Look-Up table.The Look-Up table is provided in FIG. 6. The SD ratio is also used toassign the first cardiovascular risk score. Steps a) and b) are repeatedafter a period of time, after which a second cardiovascular risk scoreis obtained. A lower second cardiovascular risk score compared to thefirst cardiovascular risk score is considered to be indicative of areduced risk of cardiovascular disease, while a higher secondcardiovascular risk score compared to the first cardiovascular riskscore is considered to be indicative of an increased risk ofcardiovascular disease.

If a higher second cardiovascular risk score is obtained, the inventionprovides for i) adjusting the dosage of the drug(s), or ii) prescribingand/or administering an additional drug(s) for; or iii) performing asurgical intervention, such as bariatric surgery.

In a second aspect, the invention provides compositions and tools forassessment, therapy, monitoring and treatment of MS. Those skilled inthe art will recognize, given the benefit of the present disclosure,that the tools for addressing Type 2 diabetes and MS as disclosed hereinhave similarities in that they both encompass personalized scoringsystems which have in common determining all of or portions the SDratio. In connection with this, the invention provides for determinationand use of the SD ratio in an index, termed herein the “FS index” (forFayad/Schentag index). In particular, the invention providescompositions and methods of treatment for patients with MS and reducingthe cardiovascular risk thereof, wherein patient specific calculationsof SD ratio and FS index as further described below enable applicationof treatments which improve or resolve metabolic syndrome and lowerassociated cardiovascular risk, and also identify drug therapybeneficial to the resolution or control of metabolic syndrome, asdefined by FS index measurements.

In one embodiment, the FS index is calculated and used in a method fordetermining cardiovascular risk for an individual suspected of having,at risk for, or diagnosed with MS. The method comprises some or all ofthe following steps:

a) obtaining from an individual one or more biological parameters, andfrom the biological parameters:

b) determining the FS index, wherein the FS index is calculated as:

$\mspace{79mu} \frac{\begin{matrix}{{0.11\left( {{FBG} + {TG}} \right)} + {{HBA}\; 1c \times \frac{\text{?}}{4}} +} \\{{{BMI} \times \frac{\text{?}}{\text{?}}} + {{AST} \times \frac{\text{?}}{\text{?}}} + {{FB}\mspace{14mu} {insulin} \times \left( {{BMI}\text{-}22} \right)}}\end{matrix}}{{S/D}\mspace{14mu} {ratio}}$?indicates text missing or illegible when filed

wherein the FBG is Fasting Blood Glucose in mg/dl; the TG isTriglycerides in mg/dl; the HBA1c is hemoglobin A1c in %; the BMI isbody mass index in kg/m²; AST is Aspartate Transferase in IU/liter; FBinsulin is fasting Blood insulin concentration in nmol/liter.

The SD ratio, as explained above, is a ratio of Glucose Supply Index (S)to Insulin Demand Index (D) calculated as follows:

1+[aggregate of carbohydrate exposure(CE)+hepatic glucoseuptake(HGU)+hepatic gluconeogenesis(GNG)and+insulin resistance(IR)],

and (D) calculated as follows:

1+[aggregate of peripheral glucose uptake(PGU)+peripheral insulinexposure(PIE)].

The FS ratio results in a numerical value that is assigned to theindividual. An FS index value of greater than 60 is indicative that theindividual is in need of therapy for MS or at risk for at least onecardiovascular complication associated with MS. Upon determining an FSindex value of greater than 60, the invention further comprisesadministering a drug to the individual. It is preferable that the drugbe pH-encapsulated glucose, wherein the glucose is released in theintestine at jejunum or ileum wherein the conditions for release of pHencapsulated glucose are a pH reading at or above a pH of 7.0. pHencapsulated glucose meeting these functional criteria is also referredto herein from time to time as BRAKE, or Brake. In one embodiment, thepH encapsulated glucose delivered in this manner so as to provide an SDvalue of 3.5 (in contrast to an individual who is not undergoing anydrug therapy, wherein the SD value is assigned as “1.0”). In anotherembodiment, the individual is treated with bariatric surgery, whichprovides an SD value of 4.0. The SD values of 3.5 for Brake and 4.0 forbariatric surgery were defined from experiments conducted on patientswith metabolic syndrome as disclosed herein. The SD values for thediabetes drugs are derived from the data of patients treated and used todevelop the lookup table disclosed herein. Values for SD and FS arepredictive of cardiovascular risk in metabolic syndrome patients.

In additional embodiments, combinations of pH encapsulated glucose canbe used alone or in combination with additional anti-diabetes and/oranti-MS drugs. Combining such agents result in a higher SD ratio, whichin turn results in a lowering of the FS index, which is favorable to thepatient in need of lower cardiovascular risk. In certain embodiments,drug combinations have an additive, or greater than additive effect onthe SD value. In certain embodiments, pH encapsulated glucose isbeneficially additive on SD when combined with either Metformin orSitagliptin, or with any member of the class of DPP-IV inhibitors insaid combinations thereof. Additional agents that can be used in drugcombinations include but are not necessarily limited to statin drugs,Insulins, hormones, GLP-1 drugs, lipids, proteins, amino-acids, andother sugars or carbohydrates, and combinations thereof.

The invention includes applications for individuals who have beendiagnosed with MS, and who are being treated for MS with at least onedrug which may be intended for therapy of MS or any of its individualmanifestations. For such patients, a first FS index value can beobtained and the patient can continue treatment with the drug for aperiod of time, after which (or during) a second FS index value isdetermined by repeating the steps used for determination of the first FSindex value. A lower second FS value relative to the first FS indexvalue indicates the drug is effective for treating individual's MS.However, a higher second FS value relative to the first FS index valueindicates that the individual is in need of a change in dosing of thefirst drug, or a change to a different drug, or is a candidate forbariatric surgery.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a pictorial representation of the glucose supply and insulindemand model set forth in the instant invention.

FIG. 2 is a graphical representation of a standard insulin concentrationtime profile for Type 2 diabetes (T2D).

FIG. 3A is a graphical representation of HGU insulin-dose responserelationship.

FIG. 3B is a graphical representation of PGU insulin-dose responserelationship. FIG. 3C is a graphical representation of GNG insulin-doseresponse relationship.

FIG. 4 is a graphical representation of standard+insulin glargineconcentration time profile (T2D).

FIG. 5 is a chart illustrating the combined impact of the HbA1c and SDratio on cardiovascular (CV) event.

FIG. 6 is the Look-Up table.

FIG. 7 provides baseline risk scoring for illustrative patient AB.

FIG. 8 provides risk scoring for illustrative patient AB under insulinglargine therapy.

FIG. 9 provides risk scoring for illustrative patient AB under metformintherapy.

FIG. 10 provides risk scoring for illustrative patient AB as if Roux-enY gastric bypass surgery had been performed.

FIG. 11 provide FS index values, BMI, HBA1c/SD ratio (HBSD) in 50 T2Dpatients with cardiovascular events (winwCombevent_heart_ml_ip) over 5years of monitoring.

FIG. 12 shows data of patients with diabetes, but who do not have CVevents. These patients differ in FS index values overall from patientswith diabetes that do have CV events. Something goes here, or close up .. . .

FIG. 13 displays the rank ordered CV output signal over our combinedpatient populations that comprise this model.

FIG. 14 illustrates the use of the neural net model applied to a T2Dpopulation of 61 patients initially treated with metformin alone, and acalculation of parameters such as FS index, HBA1c/SD ratio, and acalculated cumulative CV risk.

FIG. 15 shows the improvement in FS index in 100 RYGB surgery patients,with almost a complete lowering of CV risk to normal by 6-12 monthsafter the RYGB surgery procedure.

FIG. 16 shows the improvement in FS index and other parameters ofmetabolic syndrome for 18 patients treated with Brake, which is an oralmimetic of the hormonal effects of bariatric surgery

FIG. 17 shows FS index calculations to define the response to exenatide,trade-named Byetta. The response to this incretin hormone drug (a formof GLP-1 therapy) was a progressive lowering of FS index and a smallchange in BMI and HBSD.

FIG. 18 illustrates the FS index response to treatment with sitagliptin,aka Januvia, in 12 T2D patients treated therewith and with the data usedto analyzed changes in FS index produced by the treatment.

FIG. 19 shows an example of a weight reduction tracked on an I-padapplication for one patient over a time of 80 days. This subject, a 55year old female, was on a weight reduction program only and did not haveFS index abnormalities beyond a mild form of dietary associatedmetabolic syndrome.

DETAILED DESCRIPTION OF THE INVENTION

Traditional treatments of Type 2 diabetes either increase the release ofinsulin from the pancreas, attempt to sensitize the peripheral cells toinsulin, or give additional insulin to drive more glucose into cells. Weterm these treatments insulin demand methods. Based on the underlyingmechanisms at work in Type 2 diabetes, the insulin demand methods areless than optimal, and are likely at least partly responsible forincreased cardiovascular injury in Type 2 diabetics, sincecardiovascular injury is associated with abnormal increases in glucoseinside the endothelial cells lining blood vessels, and the insulindemand methods all operate by increasing the glucose inside endothelialcells. In this regard, the common theme of the historical andcontemporary cardiovascular outcome trials in Type 2 diabetics has beena focus on the intensive reduction of the primary biomarker, HbA1C,which is a measure of glucose in red blood cells. The American DiabetesAssociation (ADA)/European Association for the Study of Diabetes (EASD)consensus algorithm has been a guiding tool for the reduction of HbA1C.This algorithm advocates the initial use of metformin with subsequentaddition and intensification of sulfonylurea and/or insulin therapies.Inherent to this pharmacotherapeutic approach, as well as those utilizedprior to the guidelines, is an imbalance toward increased insulinexposure and increased peripheral glucose disposal, but these methodsfor lowering glycemia have adverse long term effects on thecardiovascular complications of diabetes patients. Thus, according tothe present invention, more desirable methods for control of glycemiainvolve lowering the supply of glucose to endovascular cells andlowering systemic inflammation, which we term the glucose supply side ofdiabetes management. This methodology focuses treatments for Type 2diabetes on the gastrointestinal tract and the liver, and moves thetreatment approach away from more insulin given to Type 2 diabetics, whotypically already produce excess insulin. The present inventionaccordingly facilitates analysis of relationships between the effect onglucose supply and insulin demand for Type 2 diabetes drugs, andfurthermore provides methods for determining and modulatingcardiovascular risk for Type 2 diabetics so that adjustments and/orchanges in drug regimens, and/or surgical interventions can berecommended to improve cardiovascular risk by managing diabetes on theglucose supply side.

With respect to MS, and as disclosed in U.S. provisional application No.61/254,373, filed Oct. 23, 2009 (“the '373 application”), to which thepresent application claims priority and the disclosure of which isincorporated herein by reference, it has become increasingly common inthe United States, and at the time of filing the '373 application, itwas estimated that about 20-25 percent of U.S. adults were affected withMS. MS is characterized by a group of metabolic risk factors in oneperson which include but are not necessarily limited to (a) centralobesity, indicated by excessive fat tissue in and around the abdomen;(b) atherogenic dyslipidemia, indicated by blood fat disorders, mainlyhigh triglycerides and low HDL cholesterol, that foster plaque buildupsin artery walls; (c) elevated blood pressure (130/85 mmHg or higher);(d) insulin resistance or glucose intolerance; and (e) pro-inflammatorystate (e.g., elevated C-reactive protein in the blood). The syndrome isclosely associated with a generalized metabolic disorder called insulinresistance, in which the body cannot use insulin efficiently. Metabolicsyndrome is also called insulin resistance syndrome, which can lead toType 2 diabetes, which is diagnosed when hyperglycemia is present forsufficient time to elevate blood hemoglobin A1c fraction. As alsodisclosed in the '373 application, a goal of the Glucose Supply Sidesystem is to lower the risk of cardiovascular complications associatedwith the treatment of diabetes in individuals, but also includesprevention or treatment of cardiovascular, cardiopulmonary, pulmonary orrenal diseases in both individuals and in the population by improvingendothelial function and achieving protection of organs, tissues andblood vessels in indications in which control of blood pressure andlipid levels are necessary, and for the treatment of Metabolic Syndromeand insulin resistance in patients. As will be evident from thefollowing description and examples, the present invention includes useof the SD ratio for assessing, monitoring, and developing and/orimplementing MS therapies by way of its use in the FS index.

In one embodiment, the invention provides a method for determining asuitable drug combination for the treatment of Type 2 diabetes that isable to decrease cardiovascular risk. The method comprises obtainingdata from a Type 2 diabetes population, which in one embodiment in aType 2 diabetes population in which all of the Type 2 diabetics aretaking Type-2 diabetes drugs. The data are used to obtain referencelevels of the following glucose supply parameters: carbohydrate exposure(CE), hepatic glucose uptake (HGU), hepatic gluconeogenesis (GNG) andinsulin resistance (IR). The data are also used to obtain referencelevels of the following insulin demand parameters: peripheral glucoseuptake (PGU) and peripheral insulin exposure (PIE). The method furtherentails obtaining data from discrete samples of Type-2 diabetespopulations in which all individuals are being treated with one or moreType 2 diabetes drugs at a therapeutic dose. The effects of the drugs onthe glucose supply and insulin demand parameters are used to determineadjustment factors which represent the effect of each of the drugs atthe therapeutic dosage. Adjustment factors for representative Type 2diabetes drugs are presented in Table 1. The adjustment factors for eachof the drugs on each of the glucose supply and insulin demand parametersare then used to determine a Glucose Supply Index (S) for each drugcalculated as follows:

1+CE+HGU+GNG+IR,

and an Insulin Demand Index (D) calculated as follows:

1+PGU+PIE.

The Glucose Supply Index and the Insulin Demand Index form a ratio (theSD ratio) which is indicative of the relationship between the effect onthe glucose supply and on the insulin demand parameters for the one ormore drugs. SD ratios for representative Type 2 diabetes drugs arepresented in Table 1. Without intending to be bound by any particulartheory, it is considered that an SD ratio of above 1.0 is indicativethat the drug or drug combination for which the SD ratio is calculatedfunctions on the glucose supply side of Type 2 diabetes management, andis therefore beneficial to the cardiovascular system for an individualreceiving the drug or drug combination. It is also considered that an SDratio of below 1.0 is indicative that the drug or drug combination forwhich the SD ratio is calculated functions on the insulin demand supplyside of Type 2 diabetes management, and is therefore not as beneficialfor an individual receiving the drug or drug combination as compared toa drug that affects the glucose supply side. Adjustment factors for pHencapsulated glucose and for Roux-en-Y gastric bypass bariatric surgeryare also shown in Table 1. It will be recognized by those in the artthat the present invention is not necessarily limited to determiningsuitable combinations of drugs because the invention could also be usedfor analysis of SD ratios for single drugs.

TABLE 1 CE HGU GNG IR Therapeutic SD Antidiabetic inhibition uptakeinhibition reduction PIE PGU Dose Ratio Roux-en-Y 0.75 0.85 0.75 0.45−0.35 0.15 N/A 4.75 gastric bypass pH encapsulated 0.45 0.75 0.45 0.20−0.15 0.5 N/A 2.85 glucose Miglitol 0.30 0.15 0.05 0.15 0.05 0.25 300 mg1.25 Acarbose 0.30 0.15 0.05 0.15 0.05 0.25 300 mg 1.25 Metformin 0.150.40 0.35 0.38 −0.10 0.14 2000 mg 2.20 Acetohexamide 0.00 0.14 0.07 0.000.21 0.36 1500 mg 0.77 Chlorpropamide 0.00 0.14 0.07 0.00 0.21 0.36 500mg 0.77 Tolazamide 0.00 0.14 0.07 0.00 0.21 0.36 1000 mg 0.77Tolbutamide 0.00 0.14 0.07 0.00 0.21 0.36 2000 mg 0.77 Glimepiride 0.000.18 0.08 0.00 0.24 0.39 8 mg 0.77 Glipizide 0.00 0.18 0.08 0.00 0.240.39 10 mg 0.77 Glyburide 0.00 0.14 0.07 0.00 0.21 0.36 10 mg 0.77Nateglinide 0.00 0.21 0.11 0.00 0.34 0.60 360 mg 0.69 Repaglinide 0.000.16 0.07 0.00 0.20 0.31 12 mg 0.81 Pioglitazone 0.00 0.40 0.21 0.35−0.10 0.59 45 mg 1.32 Rosiglitazone 0.00 0.40 0.23 0.39 −0.10 0.70 8 mg1.27 Troglitazone 0.00 0.40 0.22 0.35 −0.10 0.67 600 mg 1.25 InsulinAspart 0.00 0.23 0.14 0.00 0.42 0.80 0.5 U/kg 0.62 Insulin Lispro 0.000.23 0.14 0.00 0.42 0.80 0.5 U/kg 0.62 Insulin Regular 0.00 0.21 0.110.00 0.33 0.64 0.5 U/kg 0.67 Insulin Isophane 0.00 0.23 0.10 0.00 0.280.40 0.5 U/kg 0.79 Insulin Aspart 0.00 0.23 0.10 0.00 0.28 0.40 0.5 U/kg0.79 Protamine Insulin Lispro 0.00 0.23 0.10 0.00 0.28 0.40 0.5 U/kg0.79 Protamine Insulin Lente 0.00 0.23 0.10 0.00 0.28 0.40 0.5 U/kg 0.79Insulin Ultralente 0.00 0.17 0.08 0.00 0.24 0.38 0.5 U/kg 0.77 InsulinGlargine 0.00 0.24 0.10 0.00 0.30 0.42 0.5 U/kg 0.78

In Table 1, under the column “CE inhibition” the adjustment factorsrepresent decreases in carbohydrate exposure caused by the designateddrug; under the column “HGU uptake” the adjustment factors representincreases in hepatic glucose uptake; under the “GNG inhibition” theadjustment factors represent decreases in hepatic gluconeogenesis; underthe column “IR reduction” the adjustment factors represent decreases ininsulin resistance; under the column “PIE” the adjustment factorsrepresent increases in peripheral insulin exposure; and under the column“PGU” the adjustment factors represent increases in peripheral glucoseuptake.

In certain embodiments, an SD ratio of greater than 1.1, 1.2, 1.25, 1.3,1.35, 1.4, 1.45 or 1.5, including all integers to the second decimalplace there between, or higher, is indicative that the drug or drugcombination for which the SD ratio is calculated functions on theglucose supply side of Type 2 diabetes management, and is thereforebeneficial for an individual receiving the drug or drug combinationproducing these values of the SD ratio.

In another embodiment, the invention provides a method for determiningmodulation of cardiovascular risk for a Type 2 diabetic who is beingtreated with at least one drug. The method comprises a) obtaining one ormore physiological parameters from the Type 2 diabetic at a first timepoint and determining the SD ratio for the at least one drug with whichthe Type 2 diabetic is being treated, and b) assigning a firstcardiovascular risk score for the individual by summing values for oneor more physiological parameters in the Look-Up table provided in FIG. 6and the SD ratio. Then steps a) and b) are repeated after a period oftime, and from the Look-Up table a second cardiovascular risk score isobtained. A lower second cardiovascular risk score compared to the firstcardiovascular risk score is considered to be indicative of a reducedrisk of cardiovascular disease. A higher second cardiovascular riskscore compared to the first cardiovascular risk score is considered tobe indicative of an increased risk of cardiovascular disease.

If a higher second cardiovascular risk score is obtained, the inventionprovides for the following options: i) adjust the dosage of the drug(s)with which the Type 2 diabetic is being treated, or ii) prescribe and/oradminister an additional drug(s) for the Type 2 diabetic; or iii)perform a surgical intervention, such as bariatric surgery, such asRoux-en-Y gastric bypass surgery.

The invention further comprises managing the Type 2 diabetic on theglucose supply side by prescribing and/or administering a compositioncomprising DPP-IV inhibitors or agents that function in a manner similarto DPP-IV inhibitors. The invention may further comprise administeringto the Type 2 diabetic a composition comprising an agent that mimicsgastric bypass surgery on the ileum. In one embodiment, an agent capableof mimicking the effects of gastric bypass surgery is a compositioncomprising pH encapsulated glucose, which formulated so as to bereleased at a pH of approximately 6.5 to 7.5, i.e., the pH environmentof the ileum. Compositions comprising pH encapsulated glucose, as wellas other encapsulated agents suitable for use in the invention aredescribed in US Patent Publication No. 20110268795; U.S. Pat. Nos.5,322,697, 5,753,253, and 6,267,988, the disclosures of each of whichare incorporated herein by reference. Alternatively, other supply sidenutrients/agents can be similarly pH encapsulated glucose so that theagents are released at pH values between 6.5 and 7.5 so as to target theileum. Non-limiting examples of such agents include metformin,sitagliptin, saxagliptin, linagliptin, probiotic organisms, statins,antibiotics, and GLP-1 mimetics such as exenatide or liraglutide.Further, any combination of agents and/or bariatric surgical proceduresmay be performed and/or recommended according to the invention.

Type 2 diabetic drugs that can be used for calculating reference,levels, SD ratios, and cardiovascular risk scores include but are notnecessary limited to Metformin, Acetohexamide, Chlorpropamide,Tolazamide, Tolbutamide, Glimepiride, Glipizide, Glyburide, Nateglinide,Repaglinide, Pioglitazone, Rosiglitazone, Troglitazone, Insulin, Aspart,Insulin, Lispro, Insulin, Regular, Insulin, Isophane, Insulin, Aspart,Protamine, Insulin, Lispro, Protamine, Insulin, Lente, Insulin,Ultralente, Insulin, Glargine, and combinations, thereof. It will berecognized by those skilled in the art that, given the benefit of thepresent disclosure, any Type 2 diabetic drug now known or hereinafterdeveloped can be analyzed and used in the method of the invention.

The Look-Up table presented in FIG. 6 provides physiological parameterswhich can be used in combination with the SD ratio to developcardiovascular risk scores that are related to a Type 2 diabetic'stherapeutic regimen. Those skilled in the art will recognize thatadditional physiological parameters may be included in the Look-Uptable, or physiological parameters may be removed from it, but any suchmodified Look-Up table that includes an SD ratio will still be usefulfor developing cardiovascular risk scores without departing from thescope of the instant invention. In one embodiment, the Look-Up tableincludes at least the SD ratio and the Hemoglobin A₁C level (alsoreferred to as HbA1c level). Further, those skilled in the art willrecognize that the risk scoring parameters (i.e., the 0, 1, 2, 3, and 4values assigned to increasing severity of risk for each risk scoringparameter) are exemplary and can be modified, adjusted and/or replacedwith any other alphanumeric characters or symbols, and in turn thesummation of the risk scoring parameters can be designated in variousways that are intended to be encompassed within the invention.

It will also be recognized that any method for determining thephysiological parameters, which are also considered to be biomarkers,can be used. For example, in one embodiment, a xerogel sensor can beused for biomarker measurement. Suitable methods of using xerogelsensors for testing, for example the breath blood or body fluids of aType 2 diabetic are described in U.S. Pat. Nos. 6,241,948 and 6,492,182and 6,589,438, which are incorporated herein by reference. DeterminingType 2 diabetes mellitus biomarkers for use in the invention can includetesting of breath biomarkers which include but are not necessarilylimited to oxygen, glucose, acetoacetate, betahydroxybutyrate, and othersuitable free fatty acids and ketone bodies well known in the art;testing isoprostane and other metabolites of prostaglandins or any otheranalytes that are considered markers of oxidative stress; Nitrousoxides, methyl nitrous oxide metabolites; cytokines, proteins,incretins, peptides, adiponectin, C-Reactive Protein, procalcitonin,troponin, electrolytes, and other markers of the inflammatory pathwaysor those of cardiovascular injury.

In various embodiments, the present invention can be carried out using asystem, which can include but is not necessarily limited to aninput/output (I/O) device coupled to a processor; a communication systemcoupled to the processor; and/or a medical computer program and systemcoupled to the processor, the medical system configured to processmedical data of a user and generate processed medical information,wherein the medical data includes one or more of anatomical data,diabetes associated biomarkers, test specimen data, biologicalparameters, health information of the user, wherein the processor isconfigured to dynamically control operations between the communicationsystem and the medical system. The operations of the communicationsystem may include one or more of a mobile device, wirelesscommunication device, cellular telephone, Internet Protocol (IP)telephone, Wi-Fi telephone, server, personal digital assistant (PDA),tablet device in the manner of an I-pad and portable computer (PC). Thecommunication system is configured to communicate one or more of themedical data and the processed medical information to a remote devicelocated one or more of on the user, in a home, in an office, and at amedical treatment facility, the remote device including one or more of aprocessor-based device, mobile device, wireless device, server, personaldigital assistant (PDA), cellular telephone, wearable device, andportable computer (PC). The system can include an analyzer coupled toxerogel-based substrates for concentration-dependent analyte detection,the analyzer including a xerogel-based sensor coupled to a processorconfigured to analyze the specimen and generate the processed medicalinformation, wherein analysis of the specimen includes correlatingparameters of the specimen with the medical data. The specimen may be abiological sample, which could include any fluid or tissue from apatient, wherein the processed medical information includes one or moreof a chemical analysis of the specimen.

A device associated with the system can include a medicament deliverysystem coupled to the processor, the delivery system including at leastone reservoir that contains at least one composition, the deliverysystem configured to administer at least one composition for use intreating the user, wherein the composition is administered under controlof the processor and the processed medical information. The deliverysystem is configured to automatically or manually administer thecomposition or medicament.

The following example is given to illustrate the present invention. Itshould be understood that the invention is not to be limited to thespecific conditions or details described in this example.

Example 1

This Example provides a description of various embodiments of theinvention that illustrate some of the methods by which data from aType-2 diabetes population in which all of the individuals are nottaking any Type-2 diabetes drugs can be analyzed to obtain referencelevels of glucose supply and insulin demand parameters, as well asmethods by which discrete samples of Type-2 diabetes populations inwhich all individuals in each sample are being treated with one Type 2diabetes drugs at a therapeutic dose can be analyzed to identifyadjustment factors for the effect of the dose, and in turn how tocalculate and use SD ratios for individual drugs.

Therapeutic targets of the glucose supply (CE, 1+2; HGU, 3; GNG, 4; IR,5) and insulin demand (PGU, 6; PIE, 7) model are presented in FIG. 1. Toidentify quantitative differences between antidiabetic agents on CE,HGU, GNG, IR, PGU, and PIE, multidatabase searches (Cochrane CentralRegister of Controlled Trials and Cochrane Register of SystematicReviews, Embase, OVID Healthstar, OVID Journals, and PubMed) wereconducted cross-referencing title and keywords for all selectedantidiabetic therapies and their respective targets.

Alpha-glucosidase, biguanide, and thiazolidinedione (TZD) studies withlong-term, pre-post design at maximal therapeutic doses were identifiedto simulate chronic administration. To maintain consistency withcardiovascular trials and accommodate known influences of hyperglycemiaand hyperinsulinemia on the respective targets,¹³⁻¹⁶ studies includingpatients with HbA1c in the range of 6-8% and body mass index (BMI)≧30kg/m² were preferentially selected. In the event multiple studies wereavailable to identify the effect of an agent on a therapeutic target,the mean percent change was used. Conversely, if there was evidence thatan agent would elicit a response on a given target but no mathematicalrepresentation of the difference was provided, conservative estimatesconsistent with the scale of other agents and the degree of glucoseregulation were instituted to best represent the expected effect.Individual agents were then characterized for the 24 h percent changefrom baseline for CE, HGU, GNG, IR, PGU, and PIE. Carbohydrate exposurewas determined as the combined effect of caloric intake and intestinalcarbohydrate absorption. Hepatic glucose uptake was defined as thereported value obtained immediately following oral glucose loading.Because GNG is known to be enhanced in the fasting state and persistentthroughout the prandial phase,^(17,18) effect was determined during thefasting state and considered equivalent throughout the prandial phase.For studies evaluating fasting glucose and insulin concentrations, anindex of IR was determined by homeostasis model assessment of insulinresistance (HOM-AIR) using the following formula: Insulin(mU/liter)×Glucose (mmol)/22.5.¹⁹ To account for known differences insecretory and uptake dynamics during the fasting and prandial phases,studies identifying the impact of therapies during the fasting state (orsimulated hyperinsulinemic euglycemic clamp) and prandial phase (or oralglucose load insulin clamp) were specifically identified for changes inPGU (glucose infusion rate) and PIE (insulin concentrations) accordingto Equation (1):

$\begin{matrix}{{{PIE}\; {24/{PGU}}\; 24} = \frac{{\left( {{Fasting}\mspace{14mu} {Change}} \right)(12)} + {\left( {{Prandial}\mspace{14mu} {Change}} \right)(12)}}{24}} & (1)\end{matrix}$

For sulfonylurea and insulin-based therapies, insulin concentration timeprofiles were obtained and superimposed on the baseline 24 h insulinconcentration time profile of T2D patients (FIG. 2) to calculate theincrease in PIE (trapezoidal rule).²⁰ Calculated increases inincremental and cumulative insulin exposure were correlated to knowninsulin dose-response effects on HGU, GNG, and PGU (FIG. 3),^(18,21)according to the equation y=mx+b. Twenty-four-hour increases in HGU,PGU, and PIE and decreases in GNG were compared to baseline values andpercent change calculated.

With alpha-glucosidase, biguanide, TZD, secretagogue, and insulintherapies characterized for their respective impacts on CE, HGU, GNG,IR, PIE, and PGU, identification of their effect on the glucose supply(decrease in CE, increase in HGU, decrease in GNG, decrease in IR) andinsulin demand (increase in PIE, increase in PGU) dynamic was assessedaccording to Equation (2), which provides the SD ratio according to theinvention:

$\begin{matrix}{{{Glucose}\mspace{14mu} {Supply}\mspace{14mu} {(S)/{Insulin}}\mspace{14mu} {Demand}\mspace{14mu} (D)} = \frac{1 + \left( {({CE}) + ({HGU}) + ({GNG}) + ({IR})} \right)}{1 + \left( {{PIE} + {PGU}} \right)}} & (2)\end{matrix}$

Alpha-Glucosidase Inhibitors (Acarbose and Miglitol). Thealpha-glucosidase inhibitors (1) have no significant effect on totalcaloric intake,²² (2) delay and decrease carbohydrate absorption,²³⁻²⁷(3) have not been directly evaluated for HGU, (4) have negligible effecton hepatic glucose output,^(28,29) (5) reduce IR^(22,30-33) (6) havevariable effects on PFU,^(22,28,30,34-38) and (7) reduce plasma insulinconcentrations.^(22,39-45) Studies meeting the review criteria for thetarget effects of the alpha-glucosidase inhibitors on the respectivetargets are summarized here. Estimates for the effect ofalpha-glucosidase inhibitors on the respective targets are presented inTable 1.

Caloric Intake and Intestinal Carbohydrate Absorption

Meneilly and associates evaluated the effects of acarbose on totalcaloric intake by means of 3-day food recall and dietician interview.²²Acarbose was administered at an initial dose of 50-100 mg three timesdaily. At the conclusion of 52 weeks of acarbose therapy, there was nosignificant change in proportion of calories as carbohydrate(−0.7±0.8%), fat (0.9±0.8%), or protein (−0.5±0.5%), nor was there asignificant change in total caloric intake (90±50 kcal). Radziukevaluated the effect of 0, 50, and 100 mg of acarbose on the absorptionof the glucose moiety of sucrose in overnight-fasted subjects receivinglabeled 100 g oral sucrose load ([1-⁻¹⁴C]glucose) and simultaneousintravenous infusion of [3-³H]glucose.²⁴ Acarbose increasedmalabsorption in a dose-dependent manner; at 50 mg there was a modesteffect (6%), whereas at 100 mg it was approximately 30%, and at thehighest 150 mg dose approximately 66%. These findings are supported bySobajima, where carbohydrate malabsorption, measured by hydrogenexcretion following 2-month acarbose administration (50-100 mg threetimes daily) was estimated to be 31.6% of baseline.²⁵

Hepatic Glucose Uptake and Hepatic Gluconeogenesis

No studies directly evaluate the impact of alpha-glucosidase inhibitorson HGU. However, evidence does suggest acarbose delays carbohydrateabsorption^(26,27) and increases glucagon-like peptide-1secretion.^(46,47) Therefore, it would be anticipated thatalpha-glucosidase inhibitors would exhibit modest effects on retainingcarbohydrate in the splanchnic area. Likewise, there is limited dataregarding the impact of alpha-glucosidase inhibitors on hepatic GNG.Schnack evaluated the effect of long-term miglitol therapy on hepaticglucose output in poorly controlled T2D patients (HbA1c=9.9%). Aftereight weeks of therapy (300 mg/day), miglitol had no significant effecton hepatic glucose output versus placebo (0.37±0.15 versus 0.35±0.17mg/kg⁻¹/min⁻¹) under euglycemic clamp conditions.²⁸ Sels evaluated theeffects of miglitol on fasting plasma glucose (FPG) in T2D patients.Finding similar results, 200 mg of miglitol at bedtime for 1 week wasnot associated with a change in hepatic glucose production.²⁹

Insulin Resistance

In the study by Meneilly, IR was assessed at baseline and after 12months of acarbose (HOMAIR). IR was significantly improved followingacarbose treatment (6.1±0.5 versus 5.0±0.5).²² At the same acarbosedose, Calle-Pascual observed reductions in FPG and fasting plasmainsulin (FPI) and a slightly greater reduction in IR (˜27%), ascalculated by HOMAIR, after 16 weeks of therapy.³⁰ Concurrent with theseresults, Delgado observed an approximate 15% reduction in IR after 16weeks of therapy at a lower therapeutic dose of acarbose (100 mgdaily).³³ Contradicting the findings of the previous authors, Hanefeldas well as Fischer both found no significant alterations in IR.^(38,41)

Peripheral Glucose Uptake

Kinoshita evaluated the effect of acarbose 300 mg daily on glucoseutilization rate (M value) (mg/kg⁻¹/min⁻¹) under euglycemichyperinsulinemic conditions.^(37,48) After allowing the HbA1c to fall to≦8%, baseline clamp study was performed, with follow-up study at 6months. At the conclusion of therapy, glucose utilization rate wasincreased (8.00±1.96 versus 9.94±2.35 mg/kg⁻¹/min⁻¹). At the same dailydose for 16 weeks, Fischer observed a nonsignificant increase in glucosedisposal rate during euglycemic hyperinsulinemic clamp (3.2 versus 2.3mg/kg⁻¹/min⁻¹).³⁸ In the study by Meneilly, glucose infusion rate duringthe final 20 min of the 2 h hyperglycemic clamp (5.4 mM above basal) wasassessed at baseline and after 12 months of therapy. Glucose infusionrate increased significantly after acarbose therapy (1.68±0.19 versus2.69±0.19 mg/kg⁻¹/min⁻¹).²² Despite this evidence, multiple studiesunder similar experimental conditions do not confirm the observedincreases in peripheral glucose disposal after sustainedalpha-glucosidase therapy.^(28,35,36,49)

Peripheral Insulin Exposure

Numerous studies have identified a reduced postprandial insulin responsefollowing acarbose administration to T2D patients.⁴²⁻⁴⁵ Meneilly as wellas Hanefeld have both evaluated the combined fasting and postprandialeffects of long-term acarbose administration.^(22,41) Meneilly assessedfasting and postprandial insulin secretion at baseline and 12 months ofacarbose therapy (100 mg three times daily), observing significantdecreases in both increments (−13±4 and −271±159 pmol/liter,respectively).²² Hanefeld evaluated the effect of acarbose therapy (100mg three times daily) on the 24 h insulin concentration time profile.After 16 weeks of therapy, acarbose was not found to change the 24 harea under the curve of insulin from baseline.⁴¹

Biguanides (Metformin)

Metformin has been shown to (1) reduce caloric intake, (2) have variableeffects on intestinal carbohydrate absorption, (3) increase HGU, (4)diminish hepatic GNG, (5) reduce IR, (6) increase PGU, and (7) reduceinsulin exposure. Estimates for the effect of metformin on therespective targets are presented in Table 1.

Caloric Intake and Intestinal Carbohydrate Absorption

Anorexia is occasionally reported following the introduction ofmetformin therapy to T2D patients.⁵¹ Lee and Morley evaluated the effectof metformin on caloric intake in patients with T2D. Patients wererandomly given placebo, 850, or 1700 mg of metformin for 3 days andsubsequently evaluated for caloric intake during three consecutive 10min intake periods. Caloric intake was reduced during each eatinginterval in a dose-dependent manner. Total caloric intake during the 30min period was reduced 30% and 50% at 850 and 1700 mg, respectively.⁵²Despite the substantial reductions in caloric intake observed at therespective doses, it should be considered that the impact is thought tobe sustained with only extremely high doses (>2 g/kg⁻¹/day⁻¹).⁶° ′⁷⁹Animal and human studies to determine the impact of biguanides onintestinal carbohydrate absorption have yielded conflictingresults.⁵³⁻⁵⁹ Bailey reviewed the effects of metformin on intestinalglucose handling (absorption and metabolism) in animal and humanmodels.⁶⁰ In vitro animal studies have demonstrated metformin to cause aconcentration-dependent decrease in glucose transport at concentrationsin the millimolar range.⁶¹⁻⁶⁴ In vivo, Wilcock and Bailey observed netglucose transfer in the serosal fluid was reduced 12% in mice at adosage of 50 mg/kg (slightly greater than the maximum 3 g dose).⁶⁵ In apreparation of brush border vesicles isolated from rabbit intestine (5mM metformin), Kessler observed a nominal decrease in glucose uptake.⁶⁶In clinical studies of noninsulin-dependent diabetes mellitus patients,there is evidence to suggest that biguanides may delay the rate, but notthe extent of glucose absorption.^(58,67) During a 75 g oral glucoseload challenge with labeled [1-¹⁴C glucose], Jackson observed theabsorption of glucose to be slightly delayed, but ultimately unalteredover the 3 h study period.⁶⁷ Metformin has also been noted to increaseintestinal glucose utilization.^(68,69) Penicaud administered 350mg/kg⁻¹/day⁻¹ to obese fa/fa rats for 8 days, observing an increasedglucose utilization by 39% in the jejunum.⁶⁸ During intravenous glucosetolerance test, Bailey administered metformin 250 mg/kg⁻¹ to normalrats, observing an increased glucose utilization by 30-60% in mucosafrom different regions of the intestine.⁶⁹ Despite substantial increasesin intestinal glucose utilization induced by metformin, it must beconsidered that evidence suggests an increased lactate exposure in thehepatic portal vein.⁷⁰ The increased exposure to lactate may yieldincreased glucose-lactate cycling between the splanchnic tissues anddiminish the impact of intestinal metabolism on overall glycemia.⁶⁰

Hepatic Glucose Uptake

Iozzo evaluated the impact of metformin (2000 mg daily) androsiglitazone (8 mg daily) therapy on HGU.⁷¹ Positron-emissiontomography (PET) studies in combination with[¹⁸F]2-fluoro-2-deoxyglucose ([¹⁸F]FDG) and the insulin clamptechnique⁴⁸ were performed before treatment and at 26 weeks to assessHGU. At 90 min of the 150 min normoglycemic hyperinsulinemic period,patients were intravenously administered [¹⁸F]FDG and consecutive scansof the liver were obtained at 20 min. Although baseline HGU was notpresented, metformin and rosiglitazone similarly and significantlyincreased HGU (placebo-subtracted value=+0.008±0.004 and +0.007±0.004μmol/kg⁻¹/min⁻¹, respectively). Despite the failure of this study todefine a specific increase versus baseline in HGU following an oralglucose load, the relationship identified between metformin and TZDwould infer a similar impact.

Hepatic Gluconeogenesis

Stumvoll evaluated the metabolic effects of metformin in T2D patientsreceiving metformin 2550 mg daily.⁷² Prior to and at the conclusion ofthe 16 week treatment period, patients were fasted and assessed for therate of plasma lactate to plasma glucose conversion (GNG). Metformin wasfound to reduce the rate of conversion by 37% (7.3±0.7 versus 4.6±0.6mmol/kg⁻¹/min⁻¹) Hundal also evaluated the mechanism by which metforminreduces glucose production in patients with T2D.⁷³ To address knownmethodological limitations used in previous studies assessing GNG, twoindependent and complimentary methods (nuclear magnetic resonancespectroscopy and ²H₂O method) were employed to assess the impact ofmetformin therapy (2550 mg daily). Supporting the findings of Stumvolland associates, the rate of hepatic GNG was reduced 36% as evaluated bythe nuclear magnetic resonance method (0.59±0.03 versus 0.18±0.03mmol/m⁻²/min⁻¹) and 33% by the ²H₂O method (0.42±0.04 versus 0.28±0.03mmol/m⁻²/min⁻¹) after 3 months of treatment.

Insulin Resistance

In a meta-analysis of randomized controlled trials in people at risk forT2D, metformin reduced calculated IR (HOMA-IR) by 22.6%. In studies ofpatients with T2D and maximal therapeutic doses of metformin (Iozzo,Stumvoll, Tiikkainen, and Sharma), calculated IR was reduced38-44%.^(71,72,74,75)

Peripheral Glucose Uptake

In the aforementioned analysis by Stumvoll, it was noted that the rateof plasma glucose turnover (hepatic glucose output and systemic glucosedisposal) was reduced with metformin from 2.8±0.2 to 2.0±0.2mg/kg⁻¹/min⁻¹.⁷² Importantly, the reduction in plasma glucose turnoverwas attributed to the reduction in hepatic glucose output; systemicglucose disposal did not change.⁷² Corroborating evidence that metformindoes not substantially increase PGU, both Tiikkainen and Inzucchiobserved nominal increases with long-term administration of metformin attherapeutic doses. Tiikkainen clamped patients at 144 mg/dl before andafter 16 weeks of metformin 2000 mg daily. The glucose rate ofdisappearance remained unchanged (0.09±0.01 versus 0.10±0.01mg/kg⁻¹/min⁻¹).⁷⁴ Inzucchi clamped patients at 100 mg/dl before andafter 12 weeks of metformin 2000 mg daily. During the euglycemichyperinsulinemic clamp period, glucose infusion rate was increased 13%(240 versus 272 mg/m⁻²/min⁻¹).⁷⁸

Peripheral Insulin Exposure

Metformin was consistently found to reduce FPI concentrations (range:10-30%). In the aforementioned studies by Iozzo and Stumvoll, FPI wasreduced ˜30% (63±12 to 43.0±5.0 pmol/liter) and 17% (12±5 to 10±μU/ml),respectively.^(71,72) Tiikkainen observed an ˜30% reduction in FPI (13versus 9 mU/liter), and Sharma found an ˜10% reduction (76.0±54.5 to69.0±45.0 pmol/liter) following administration of metformin 2000 mgdaily for 16 weeks.^(74,75) Evaluating both the fasting and mealtimeeffects of metformin, Inzucchi found mean fasting and postprandialplasma insulin concentrations to be slightly, but not significantlyreduced with metformin 2000 mg daily for 12 weeks.⁷⁸

Thiazolidinediones (Pioglitazone, Rosiglitazone, and Troglitazone)

The TZD agents (1) have no significant effect on total caloric intake,(2) have no evidence for diminished intestinal absorption, (3) increaseHGU,^(71,83,84) (4) diminish hepatic GNG, (5) reduce IR, (6) increasePGU, and (7) reduce insulin exposure. Studies meeting review criteriafor the target effects of the TZDs are presented here. Estimates for theeffect of TZDs on the respective targets are presented in Table 1.

Caloric Intake and Intestinal Carbohydrate Absorption

The effect of TZDs on caloric intake has been evaluated in T2D patientstreated with pioglitazone and rosiglitazone. Smith estimated subjectivemeasures of hunger (visual analog scale) and satiety in patients treatedwith pioglitazone 45 mg/day.⁸⁰ At the conclusion of 24-weeks,pioglitazone demonstrated no effect on hunger and satiety. Strowig andRaskin assessed caloric intake via food records in patients administeredrosiglitazone 4 mg twice daily.⁸¹ At the conclusion of 32-weeks, meancaloric intake did not differ between treatment groups (rosiglitazone2066.4±589.2 and 1994.9±726.5 calories/day for baseline and week 32,respectively). The effect of troglitazone on caloric intake in patientswith diabetes has not been directly evaluated. However, in healthyvolunteers, Cominancini evaluated the effects of troglitazone 400 mgdaily.⁸² Troglitazone was not associated with changes in carbohydrate ortotal caloric intake after 2 weeks of therapy.

Hepatic Glucose Uptake

Bajaj and Kawamori have both evaluated the effect of pioglitazone onHGU.^(83,84) Kawamori administered pioglitazone 30 mg daily to patientstreated with either diet alone or sulfonylurea therapy. Following 12weeks of therapy, the rate of splanchnic glucose uptake increased from28.5±19.4% to 59.4±27.1% (p=0.010). Bajaj administered pioglitazone 45mg once daily after a 48 h medication washout period. At 16 weeks,splanchnic glucose uptake increased from 33.0±2.8% to 46.2±5.1%.⁸³ Aspreviously mentioned, Iozzo evaluated the effects of rosiglitazone onHGU, utilizing the insulin clamp technique and PET studies. After 26weeks, rosiglitazone 4 mg twice daily significantly increased HGU versusplacebo (+0.007 mmol/min⁻¹/kg⁻¹). Since the study did not presentbaseline data to allow for percent change calculation, rosiglitazone wasconsidered to have similar characteristics to pioglitazone for HGU.Troglitazone has not been directly evaluated for impact on HGU and wasconsidered comparable to pioglitazone and rosiglitazone.

Hepatic Gluconeogenesis

Gastaldelli evaluated the fasting and mixed-meal effects of pioglitazoneand rosiglitazone on hepatic GNG.^(17,85) Pioglitazone 45 mg daily for16 weeks reduced fasting endogenous glucose production (13.1±0.3 versus12.0±0.6 7 mmol/min⁻¹/kg⁻¹) and GNG contribution (73.1±2.4% versus64.4±3.1%). During the mixed meal, endogenous glucose production wasagain reduced (6.5±0.7 versus 5.4±0.7 mmol/min⁻¹/kg⁻¹) as was thecontribution of GNG to the total rate of appearance (45.6±1.7% versus41.3±2.6%).¹⁷ In the second study, rosiglitazone 8 mg daily for 12 weeksreduced fasting endogenous glucose production (18.6±0.9 versus 16.3±0.6mmol/min⁻¹/kg⁻¹) and GNG contribution (67±4% versus 59±3%). The directeffect of troglitazone on hepatic GNG has not been evaluated. However,Inzucchi evaluated the effect of troglitazone on endogenous glucoseproduction and found no significant difference after administration oftroglitazone 400 mg daily for 12 weeks.⁷⁸

Insulin Resistance

Langenfield evaluated the effect of pioglitazone on IR as determined byHOMA-IR.⁸⁶ Pioglitazone at a dose of 45 mg daily for 24 weeks in T2Dpatients resulted in a decrease in IR from 6.15±4.05 to 3.85±1.92.Comparative analyses have identified similar effects of pioglitazone androsiglitazone on IR. In a 12-week trial of pioglitazone 45 mg daily androsiglitazone 4 mg twice daily, Goldberg reported a reduction from8.2±0.3 to 5.4±0.2 and 7.8±0.4 to 4.8±0.2, respectively.⁸⁷ Under thesame experimental design, Deeg observed similar reductions in IR forpioglitazone and rosiglitazone (8.3 versus 5.4 and 7.9 versus 4.7,respectively).⁸⁸ Yatagai evaluated the effects of troglitazone 400 mgdaily on IR (HOMA-IR). After 12 weeks, IR was reduced from 5.7±0.7 to4.5±0.8.⁸⁹

Peripheral Glucose Uptake

Pioglitazone, rosiglitazone, and troglitazone have been shown toincrease basal and incremental PGU. Bajaj observed the glucose infusionrate to be significantly greater during euglycemic insulin clamp (5.6mmol/liter) after treatment with pioglitazone 45 mg daily for 16 weeks(6.9±0.5 versus 5.0±0.5 mg/kg⁻¹/min⁻¹).⁸³ Glucose infusion rate was alsosignificantly increased during the 180-420 min period of the 75 g oralglucose load-insulin clamp (5.3±0.5 versus 2.9±0.5 mg/kg⁻¹/min⁻¹).Tiikkainen demonstrated that rosiglitazone 4 mg twice daily for 16 weeksincreased glucose disposal rate (0.10±0.02 versus 0.17 mg/kg⁻¹/min⁻¹)with glycemic maintenance at ˜8 mmol/liter.⁷⁴ Inzucchi foundadministration of troglitazone 400 mg daily for 12 weeks significantlyincreased glucose disposal rate (172 versus 265 mg/m⁻²/min⁻¹) during thefinal hour of hyperinsulinemic-euglycemic clamp study (5.6mmol/liter).⁷⁸

Peripheral Insulin Exposure

Gastaldelli evaluated the effect of pioglitazone 45 mg daily for 16weeks on the metabolic and hormonal response to a mixed meal in T2Dpatients.¹⁷ Fasting plasma insulin and plasma insulin during the mixedmeal challenge (0-6 h) were similarly reduced versus baseline (88 versus81 pmol/liter and 268 versus 248 pmol/liter, respectively). Miyazakievaluated the dose-response effect of 7.5-45 mg of pioglitazone onfasting insulin secretion after 26 weeks. Fasting plasma insulinconcentrations were similarly reduced (15-25%) at the respectivepioglitazone doses.⁹⁰ Miyazaki and DeFronzo have reported thatrosiglitazone demonstrates similar effects to pioglitazone on insulinsecretion.⁹¹ After 3 months of therapy with rosiglitazone 8 mg daily,FPI was reduced (18±1 versus 13±1 μU/ml) without change in the meaninsulin concentration (37±4 versus 36±4 μU/ml) during a 2 h oral glucosetolerance test (OGTT). Pioglitazone similarly reduced FPI (15±1 versus13±2 μU/ml) and also demonstrated no change in mean insulinconcentration during a 2 h OGTT. Yatagai evaluated the effects oftroglitazone 400 mg daily on FPI concentration in T2D patients.⁸⁹ After12 weeks of therapy, FPI concentration was found to be slightly reduced(14.3±2.1 to 12.9±2.6 μU/ml). Similarly, Inzucchi evaluated the effectsof troglitazone 400 mg daily for 12 weeks.⁷⁸ At the conclusion of thestudy, fasting and postprandial plasma insulin concentrations werereported to be slightly, but not significantly, reduced.

Secretagogues and Exogenous Insulin

Secretagogues and exogenous insulin (1) have variable effects on caloricintake,⁹²⁻¹⁰⁴ (2) have no evidence for diminished intestinalcarbohydrate absorption, (3) increase HGU,²¹ (4) diminish GNG,^(18,21)(5) have variable effects on IR,^(38,103,105-112) (6) increase PGU,²¹and (7) increase PIE.¹¹³⁻¹²²

Caloric Intake and Intestinal Carbohydrate Absorption

It has been hypothesized that increased plasma insulin concentrationsincrease appetite and cause undesirable weight gain.⁹²⁻⁹⁵ The UKPDS andother studies in T2D patients have demonstrated that initiation ofinsulin is often accompanied by duration and intensity dependent weightgain (5-10%).⁹⁶⁻¹⁰⁰ The potential cause of increased weight gain hasbeen attributed to increased caloric intake secondary tohyperinsulinemia or hypoglycemic fear and also a reduction in the basalmetabolic rate.^(97,101,102) However, it must be considered that weightgain is not a universal finding and that modest reductions in dailycaloric intake have been observed.^(103,104) Moreover, insulin therapyis commonly, but not unequivocally, associated with increased caloricintake and subsequent weight gain.

Standard and Insulin Concentration Time Profiles

Gannon and Nuttall identified the 24 h insulin secretion profile inpatients with T2D prior to initiating dietary control measures (FIG. 2).On average, patients were aged 63 years (range 51-82), with a 4-yearduration of diabetes (range 1-15), BMI of 31 kg/m² (range 27-36), and atotal glycosylated hemoglobin of 9.6% (range 8.6-11.2).²⁰

Hepatic Glucose Uptake, Hepatic Gluconeogenesis, and Peripheral GlucoseUptake

Basu evaluated the insulin dose-response curves for stimulation ofsplanchnic (hepatic) glucose uptake, suppression of endogenous glucoseproduction, and PGU.²¹ Patients were fed a standard 10 cal/kg meal (55%carbohydrate, 30% fat, 15% protein) and stabilized overnight at aglucose level of ˜5 mmol/liter (90 mg/dl). On the subsequent morning,insulin was infused at variable rates from 0 to 180 min (˜0.5mU/kg⁻¹/min⁻¹), 181 to 300 min (˜1.0 mU/kg⁻¹/min⁻¹), and 301 to 420 min(˜2.0 mU/kg⁻¹/min⁻¹). The insulin dose-response relationship forsplanchnic glucose uptake and PGU during the final 30 min of the low-(˜150 pmol/liter), medium- (˜350 pmol/liter), and high- (˜700pmol/liter) dose insulin infusions are presented in FIG. 3. To mostaccurately quantify the hepatic contribution to glucose supply, theinsulin dose-response relationship to hepatic GNG was utilized in placeof total endogenous glucose production. Gastaldelli evaluated the effectof physiological hyperinsulinemia on GNG in T2D.¹⁸ Under euglycemicclamp conditions, total rates of glucose appearance were calculated froma previously established two-compartmental model.¹²³ Endogenous glucoseoutput was subsequently calculated as the difference between the rate ofglucose appearance and the exogenous glucose rate. Percent contributionof GNG to the plasma glucose was calculated as the ratio of C5:²H₂Oenrichments. Under basal conditions, mean plasma insulin concentrationwas 12.2±1.2 μU/ml (˜85 pmol/liter) and increased to 113±6 μU/ml (˜780pmol/liter) during euglycemic hyperinsulinemic clamp. Endogenous glucoseoutput reduced from 15.2±0.4 to 7.1±0.9 and plasma C5:²H₂O ratiodeclined from 0.60±0.02 to 0.25±0.02. The insulin dose-responserelationship for suppression of hepatic GNG is presented in FIG. 3.

Insulin and Sulfonylurea Concentration Time Profiles

Twenty-four-hour insulin concentration time curves were obtained forsulfonylurea, meglitinide, and exogenously administered insulinproducts.¹¹³⁻¹²² Due to a lack of available evidence characterizing the24 h insulin concentration profile of first generation sulfonylureaagents, comparable dose relationships were drawn with the profile forglyburide. Twenty-four-hour steady state insulin concentration timecurves were superimposed on the baseline secretion profile of thestandard T2D patient. As an example, the concentration time profile ofinsulin glargine at a dose of 0.5 U/kg is presented in FIG. 4. Using thetrapezoidal rule, glargine increased PIE 30% versus baseline (5765versus 7495 pmol/h⁻¹/liter⁻¹, respectively). Applying the superimposed24 h insulin concentration time curve to the insulin dose-responserelationships for HGU, GNG, and PGU, glargine was observed to increaseHGU and PGU (24% and 42%, respectively), while decreasing GNG 10%.Hepatic glucose uptake, GNG, PGU, and PIE values for the remainingexogenously administered insulin products and sulfonylurea agents arepresented in Table 1.

Insulin Resistance

In 1993, Hotamisligil and colleagues identified the relationship betweeninflammation and metabolic conditions, such as obesity and IR, bydemonstrating adipocyte expression of the pro-inflammatory cytokinetumor necrosis factor-α(TNF-α) and that expression in the adipocytes ofobese animals is markedly increased.¹²⁴ Further efforts in the area ofobesity have identified obesity to be a state of chronic inflammation,as indicated by increased plasma concentrations of C-reactive protein,interleukin-6 (IL-6), and plasminogen activator inhibitor-1(PAI-1).¹²⁵⁻¹²⁷ Dandona has characterized the anti-inflammatory effectof insulin (reduction of reactive oxygen species generation bymononuclear cells, nicotinamide adenine dinucleotide phosphate oxidasesuppression, reduced intranuclear NF-κB, suppressed plasma intercellularadhesion molecule-1 and monocyte chemotactic protein-1, reducedintranuclear Egr-1, monocyte chemotactic protein-1 and PAI-1) as well asthe link between IR, obesity, and diabetes.¹²⁸⁻¹³⁰ Crook and Pickupfirst proposed T2D to be a chronic inflammatory condition characterizedby increased concentrations of acute phase reactants (sialic acid,IL-6).^(131,132) Indeed, several studies have confirmed the presence ofinflammatory mediators predicts T2D.¹³³⁻¹³⁹ It has been noted that theincreased concentration of pro-inflammatory cytokines (i.e. TNF-α, IL-6)associated with obesity and T2D may interfere with insulin action bysuppressing signal transduction. Therefore, the anti-inflammatoryeffects of insulin may be blunted, which in turn may promoteinflammation.¹³⁰

The extensive characterization of obesity and T2D as inflammatoryconditions with blunted anti-inflammatory (and possiblypro-inflammatory) effects of insulin creates inconsistency whencharacterizing insulin's effect on IR. It has been argued that, byincreasing weight gain, insulin therapy would exacerbate IR.¹¹² So too,there is conflicting evidence that insulin and sulfonylurea agents haveno significant effect, or alternatively a beneficial effect, on IR asassessed by HOMA-IR. Contradictory evidence in combination with knownpathophysiologic evidence would indicate a net neutral effect of insulinon IR.

References for Example 1

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Example 2

This Example illustrates that, in accordance with the method of theinvention, patients managed on the glucose supply side would have fewercardiovascular events versus those managed on the insulin demand side.In order to test this, the electronic medical records of a group modelhealth maintenance organization were queried to compile a population ofpatients meeting the following inclusion criteria: (i) T2D; (ii) knowndate of T2D diagnosis; (iii) ICD-9 or CPT code identification and chartreview confirmation of a first major cardiovascular event (myocardialinfarction, coronary artery bypass graft, or angioplasty); (iv) 5 yearsof continuous eligibility, and

(v) on antidiabetic therapy at the beginning of the 5-year observationperiod. These patients were subsequently matched (1:1) to T2D patientsmeeting the same criteria that had not experienced an event and analyzedfor differences in glucose control (HbA₁C), the glucose:insulin supplydynamic (SD ratio), and categorical combinations of both parameters.

To help explain the situation where long-term, cardiovascular outcometrials have resulted in counterintuitive outcomes, we have in Example 1presented data for a pharmacokinetic/pharmacodynamic model thatcharacterizes the effect of conventional antidiabetic therapies on theglucose supply and insulin demand dynamics. To determine ifpharmacotherapeutic strategies that favor the glucose supply or insulindemand dynamic are associated with cardiovascular benefit, weretrospectively identified patients with 5-years of eligibility prior toexperiencing an initial event, matched them to patients not experiencingan event, and assessed the impact of the glucose supply:insulin demand(SD) ratio in conjunction with measured glucose control (HbA1c).

Methods.

The supporting literature and methods used to calculate the SD ratio foreach of the antidiabetic agents included in this Example was describedin Example 1. In this Example, to test whether patients managed on theglucose supply side would have fewer cardiovascular events versus thosemanaged on the insulin demand side, the electronic medical records of agroup model health maintenance organization were queried. From theelectronic medical record, de-identified health care claims, medicalprogress notes, and laboratory data with dates of service spanning Jan.1, 1997, and Dec. 31, 2008, were reviewed to compile a population ofpatients meeting the following inclusion criteria: (i) T2D; (ii) knowndate of T2D diagnosis; (iii) ICD-9 or CPT code identification^(5,6) andchart review confirmation of a first major cardiovascular event(myocardial infarction, coronary artery bypass graft, or angioplasty);(iv) 5 years of continuous eligibility, including medical andprescription claims, preceding the initial cardiovascular event; and (v)on antidiabetic therapy at the beginning of the 5-year observationperiod. From the database of 194,268 patients, an initial queryidentified 16,007 patients (8.2%) to have ICD-9 code 250 in theirmedical claims history. Of these, 15,349 (95.9%) were confirmed to havea diagnosis of T2D and 11,751 to have a diagnosis date referenced intheir medical history. Within the group of patients with T2D and a knowndate of diagnosis, 1107 had an initial event, and 50 met the finalinclusion parameters of 5 years of continuous medical and prescriptionclaims preceding the event and presence of antidiabetic therapy at theindex date. These patients were subsequently matched (1:1) to T2Dpatients meeting the same criteria that had not experienced an event.Primary baseline matching criteria included age, gender, T2D duration,BMI, and HbA1c. Secondary matching criteria included a composite profileof blood pressure (systolic, diastolic) and cholesterol [low-densitylipoprotein, high-density lipoprotein, triglycerides (TG)]. All baselinevalues were determined, as an average, from the first 6 months of the5-year observation period. The University at Buffalo's Health SciencesInstitutional Review Board previously approved the de-identifieddatabase for exempt status; informed consent was not required.

Based on the evidence presented in the aforementioned cardiovascularoutcome trials in the T2D population, it was not anticipated thataverage HbA1c or categorical HbA1c breakpoints would be independentlyassociated with a reduction in cardiovascular outcomes. Similarly,because the SD ratio is a measure of the pharmacologic impact on glucosesupply and insulin demand dynamics, it was not anticipated that theaverage SD ratio or categorical SD ratio breakpoints would beindependently associated with a reduction in events. However, wereasoned that combing the optimal SD ratio breakpoint that minimizedevent rate and the ADA-recommended HbA1c breakpoint (7%) would realizethe greatest cardiovascular benefit. Therefore, in addition toevaluating the associations of mean HbA1c, categorical HbA1c (≦7% vs.<7%), mean SD ratio, and categorical SD ratios (≦1, ≦1.25, ≦1.5) withcardiovascular events, we determined the optimal SD ratio breakpointthat minimized event rate, coupled the breakpoint with the recommendedHbA1c threshold (7%), and analyzed the combined parameter for anassociation with event rate. All statistical assessments of baselinecharacteristics and cardiovascular outcomes were conducted with theStudent's t-test (continuous data) or Chi-square/Fisher's exact test(categorical data).

Results

Application of the Glucose Supply and Insulin Demand Model toCardiovascular Events. 50 patients with an initial event and known dateof occurrence were case matched with noncardiovascular event controlsper aforementioned criteria. Baseline characteristics for the event andcontrol patients are presented in Table 2.

TABLE 2 Cardiovascular event Controls p value Age (years) 64.6 ± 10.5 64.8 ± 11.0 .926 Gender (male) 25 25 1.00 Duration of T2D (years) 10.6± 5.9  10.5 ± 3.6 .885 Weight 203.5 ± 50.5  203.1 ± 46.5 .972 BMI(kg/m²) 32.4 ± 7.1  32.5 ± 6.4 .958 Systolic blood pressure 142.2 ±14.3  145.0 ± 13.5 .308 (mmHg) Diastolic blood pressure 81.1 ± 8.6  82.4± 9.8 .466 (mmHg) Low-density lipoprotein 114.2 ± 29.9  117.5 ± 23.6.536 (mg/dl) High-density lipoprotein 43.0 ± 10.9  46.4 ± 11. 3 .129(mg/dl) TG (mg/dl) 288.8 ± 313.1  176.0 ± 81. 8 .017 FPG (mg/dl) 156.7 ±49.5  163.4 ± 51.9 .510 HbA1c (%) 7.7 ± 1.4  7.5 ± 1.19 .484 SD Ratio1.1 ± 0.3  1.2 ± 0.3 .051 ACEI/ARB (%) 32.5 ± 43.6  47.6 ± 45.2 .090Statin (%) 29.1 ± 40.3  41.3 ± 39.5 .130 ACEI = angiotensin convertingenzyme inhibitor, ARB = angiotensin receptor blocking agent, BMI = bodymass index, FPG = fasting plasma glucose, HbA1c = hemoglobin A₁C, SD =glucose supply:insulin demand, TG = triglycerides

Age, gender, duration of T2D, and metabolic characteristics were similarbetween groups, with the exception of TG that were significantly higherin the cardiovascular event cohort (288.8±313.1 mg/dl versus 176.0±81.8mg/dl; p=0.017). No significant differences in nondiabetes-relatedtherapies were observed between groups, although more control patientstended to be on angiotensin-converting enzyme inhibitors/angiotensinreceptor blocking agents (47.6±45.2% vs. 32.5±43.6%; p=0.090) and alsoto have higher SD ratio values at baseline (1.2±0.3 vs. 1.1±0.3;p=0.051).

Over the course of the 5-year observation period, there was nosignificant difference observed for the average HbA1c between eventpatients and controls (7.5±1.0% vs. 7.3±0.9%; p=0.275, respectively).There was also no difference in event rate between the cohorts whenpatients were categorized at the HbA1c≧7% breakpoint (72% vs. 64%;p=0.391, respectively). Like HbA1c, the mean SD ratio was notsignificantly different between the cohorts (1.2±0.3 vs. 1.3±0.3;p=0.205, respectively), and there was also no difference in event ratebetween the cohorts at the ≧1 (68% vs. 76%; p=0.373, respectively), 1.25(42% vs. 56%; p=0.161, respectively), or 1.5 (22% vs. 30%; p=0.362)breakpoints.

We determined that more aggressive HbA1c reduction and higher SD ratiovalues were not independently associated with a reduction incardiovascular events. FIG. 5 presents data for the combined impact ofthe recommended HbA1c breakpoint (<7%) and optimal SD ratio breakpoint(1.25) on cardiovascular outcomes. Identical event rates were observedfor patients managed to an HbA1c<7% and SD ratio 1.25, HbA1c<7% and SDratio<1.25, and HbA1c 7% and SD ratio≧1.25 (44%). Compared to theremainder of the population, the only group demonstrating a trend towardgreater cardiovascular event risk were those managed at higher glucosevalues and on the insulin demand side of the model (HbA1c≧7% and an SDratio<1.25; 61% vs. 39%; p=0.096).

As can be seen from the foregoing description, the overwhelming evidencethat intensive blood glucose management does not confer a correspondingreduction in macrovascular events requires evaluation of theinterventions used to attain the reductions in HbA1c. The impact ofpharmacologic intervention has been largely dismissed in the assessmentof recent T2D cardiovascular outcome trials.¹⁻³ Close inspection oftherapies utilized during the trials demonstrates a focus on agents thatpredominantly increase PIE and peripheral glucose disposal. At baselineof the ADVANCE trial, patients in the intensive and standard groups werepredominantly on sulfonylurea- (71.8% and 71.1%) and metformin- (61.0%and 60.2%) based regimens with minimal insulin utilization (1.5% and1.4%). At end of follow-up, sulfonylurea (92.4%) and insulin utilization(40.5%) spiked in the intensive treatment group, while in the standardgroup, sulfonylurea utilization decreased (58.7%) and insulin usemoderately increased (24.1%).² Similarly, the ACCORD trial featuredgreater secretagogue and insulin exposure in those receiving intensivetherapy versus standard therapy (86.6% and 73.8% vs. 77.3% and 55.4%,respectively).¹ The VADT determined initial treatment class by BMI,metformin+rosiglitazone when ≧27 kg/m², glimepiride+rosiglitazone when<27 kg/m². Subsequently, the intensive management cohort receivedmaximal doses, while standard therapy received one-half the maximaldose.³ Notably, before any changes in oral medications were made,insulin was added to patients in the intensive management cohort notachieving a HbA1c<6% and only to standard-therapy patients not achievinga HbA1c<9%. Thus, in summary, by analyzing the relationship betweencardiovascular events, blood glucose reduction, and the SD ratio, ourinvention indicates that for patients managed at higher HbA1c values(≧7%), there may be a protective cardiovascular effect ifpharmacologically managed on the glucose supply side (SD ratio≧1.25).

References for Example 2

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Ceriello A, Lush C W, Darsow T, Piconi L, Corgnali M,    Nanayakkara N, Frias J P, Maggs D. Pramlintide reduced markers of    oxidative stress in the postprandial period in patients with type 2    diabetes. Diabetes Metab Res Rev. 2008; 24(2):103-8.-   48. Orskov L, Nyholm B, Yde Hove K, Gravholt C H, Møller N,    Schmitz 0. Effects of the amylin analogue pramlintide on hepatic    glucagon responses and intermediary metabolism in type 1 diabetic    subjects. Diabet Med. 1999; 16(10):867-74.

Example 3

This Example provides a description and illustrative examples ofexpected results obtained by implementation of the method of theinvention under several exemplary scenarios.

Illustrative Case Example: Baseline Patient Presentation: Patient AB isa 5′8, 94.3 kg (body mass index of 31.6 kg/m²), 53 yo male with newonset type 2 diabetes mellitus (T2D). Upon the advice of his physicianten years ago he quit smoking. At present, he is negative forcardiovascular complications of peripheral arterial disease, coronaryartery disease, coronary revascularization procedures, myocardialinfarction, or ischemic stroke. Clinical evaluation and consultationreveals that his blood pressure is slightly elevated (138/82 mmHg), hisdiet is high in fat and carbohydrate, and he does not participate inaerobic activities. Laboratory analysis revealed his hemoglobin A₁C tobe 7.2%, LDL cholesterol to be 102 mg/dL, HDL cholesterol to be 42mg/dL, and triglycerides to be 187 mg/dL. His current medication regimenincludes only Lisinopril 10 mg daily and Simvastatin 40 mg at bedtime.

Management Scenario 1: Patient AB is given Insulin Glargine at a dose of47 units (0.5 units/kg/day) to be administered subcutaneously once dailyat bedtime. He returns to the clinic 3-months later with no significantchange in his cardiovascular history and his blood pressure (136/84mmHg), body mass index (31.6 kg/m²), dietary habits, and physicalactivity habits have not significantly changed. His hemoglobin A₁C hasdeclined to 6.7%, while his LDL cholesterol (104 mg/dL), HDL cholesterol(43 mg/dL), and triglycerides (168 mg/dL) have not significantlychanged. His medication regimen now includes the Insulin Glargine 47units daily, Lisinopril 10 mg daily, and Simvastatin 40 mg at bedtime.

Management Scenario 2: Patient AB is given Metformin at an oral dose of1,000 mg twice daily. He returns to the clinic 3-months later with nosignificant change in his cardiovascular history and his blood pressure(136/84 mmHg), body mass index (31.6 kg/m²), dietary habits, andphysical activity habits have not significantly changed. His hemoglobinA₁C has declined to 6.7%, while his LDL cholesterol (104 mg/dL), HDLcholesterol (43 mg/dL), and triglycerides (168 mg/dL) have notsignificantly changed. His medication regimen now includes the Metformin1,000 mg twice daily, Lisinopril 10 mg daily, and Simvastatin 40 mg atbedtime.

Management Scenario 3: Patient AB elects to undergo Roux-en-Y gastricbypass. He returns to the clinic 3-months later with a hemoglobin A₁Creduction to 6.7% without significant change in his cardiovascularhistory, blood pressure (136/84 mmHg), LDL cholesterol (104 mg/dL), HDLcholesterol (43 mg/dL), triglycerides (168 mg/dL) or medications. He haslost 22.7 kg reducing his body mass index to 24.0 kg/m². Because of thesurgery he can only eat low carb, low fat meals or he gets extremelynauseous and he has yet to start exercising.

Supply/Demand Calculations for Antidiabetic Therapeutic Interventions:Supply/Demand (SD) Calculation for Metformin 2000 mg Daily (Table 3):

TABLE 3 Supply Demand Ratio (1 + (CE + (1 + (PIE + (Supply/ CategoryScore HGU + GNG + IR)) PGU)) Demand) CE 0.15 2.28 1.04 2.20 HGU 0.40 HGI0.35 IR 0.38 PIE −0.10 PGU 0.14

Supply/Demand Calculation for Glyburide 10 mg Daily (Table 4):

TABLE 4 Supply Demand Ratio (1 + (CE + (1 + (PIE + (Supply/ CategoryScore HGU + GNG + IR)) PGU)) Demand) CE 0.00 1.21 1.57 0.77 HGU 0.14 HGI0.07 IR 0.00 PIE 0.21 PGU 0.36

Supply/Demand Calculation for Pioglitazone 45 mg Daily (Table 5):

TABLE 5 Supply Demand Ratio (1 + (CE + (1 + (PIE + (Supply/ CategoryScore HGU + GNG + IR)) PGU)) Demand) CE 0.00 1.97 1.49 1.32 HGU 0.40 HGI0.21 IR 0.35 PIE −0.10 PGU 0.59

Supply/Demand Calculation for Insulin Glargine 0.5 U/kg Daily (Table 6):

TABLE 6 Supply Demand Ratio (1 + (CE + (1 + (PIE + (Supply/ CategoryScore HGU + GNG + IR)) PGU)) Demand) CE 0.00 1.34 1.72 0.78 HGU 0.24 HGI0.10 IR 0.00 PIE 0.30 PGU 0.42Supply/Demand Calculation for Insulin Glargine 1.0 U/kg daily (Table 7):

TABLE 7 Supply Demand Ratio (1 + (CE + (1 + (PIE + (Supply/ CategoryScore HGU + GNG + IR)) PGU)) Demand) CE 0.00 1.68 2.44 0.69 HGU 0.48 HGI0.20 IR 0.00 PIE 0.60 PGU 0.84Supply/Demand Calculation for Metformin 2000 mg daily+Insulin Glargine0.5 U/kg daily (Table 8):

TABLE 8 Supply Demand Ratio (1 + (CE + (1 + (PIE + (Supply/ CategoryScore HGU + GNG + IR)) PGU)) Demand) CE 0.15 2.62 1.76 1.49 HGU 0.64 HGI0.45 IR 0.38 PIE 0.20 PGU 0.56Supply/Demand Calculation for pH encapsulated glucose, 3.0 grams/daybetween meals (Table 9):

TABLE 9 Supply Demand Ratio (1 + (CE + (1 + (PIE + (Supply/ CategoryScore HGU + GNG + IR)) PGU)) Demand) CE 0.45 2.85 1.00 2.85 HGU 0.75 HGI0.45 IR 0.20 PIE −0.15 PGU 0.15

Supply/Demand Calculation for Roux-en-Y Gastric Bypass (Table 10):

TABLE 10 Supply Demand Ratio (1 + (CE + (1 + (PIE + (Supply/ CategoryScore HGU + GNG + IR)) PGU)) Demand) CE 0.75 3.80 0.80 4.75 HGU 0.85 HGI0.75 IR 0.45 PIE −0.35 PGU 0.15A Risk scoring table for Predicting Macrovascular Events (MyocardialInfarction, Stroke, CV-related Death) in Patients Afflicted with Type 2Diabetes Mellitus (the Look-Up table) is presented in FIG. 6. BaselineScoring for Patient AB is presented in FIG. 7. The risk scoring tablefor Management Scenario 1 (Insulin Glargine 47 units to be administeredsubcutaneously once daily at bedtime) is presented in FIG. 8. The riskscoring table for Management Scenario 2 (Metformin 1,000 mg orally twicedaily) is presented in FIG. 9. The risk scoring table for ManagementScenario 3 (Roux-en-Y Gastric Bypass) is presented in FIG. 10.

Case Summary and Rationale for Supply Side Management of T2D:

Pharmacotherapeutic Management Strategies (Scenario 1 and Scenario 2)

In the above case summary three management strategies are presented forexample patient AB. In the first management example, utilizing InsulinGlargine at a dose of 47 units daily (0.5 units/kg/day) was able tosignificantly reduce the hemoglobin A₁C, but was not able to diminishthe macrovascular event score because of an increase in theSupply/Demand score with all other variables being held constant.Conversely, the Metformin regimen was able to reduce the macrovascularevent score at the same level of hemoglobin A₁C lowering because theSupply/Demand score was also reduced while holding all other variablesconstant. In this particular patient case, there was no occurrence ofworsening dietary habits, weight gain, and/or hypoglycemic events thatare common with anti-diabetic agents such as Insulin and Secretagogues,but not Amylinomimetics, Alpha-glucosidase Inhibitors, Bile-acidSequestrants, Dopamine Agonists, DPP-IV inhibitors, GLP-1 agonists,Metformin, and Thiazolidinediones. Therefore, because worsening dietaryhabits, weight gain, and/or hypoglycemic events would elevate themacrovascular event risk score, it is possible that macrovasculardetriment may be seen when administering Insulin and Secretagoguetherapy despite an improvement in the hemoglobin A₁C. Moreover, thisSupply side model of macrovascular disease progression in the T2Dpatient provides both an explanation for why neutral/poor outcomes wereobserved in large-scale randomized controlled trials that moreaggressively reduced hemoglobin A₁C and also a therapeutic algorithm tomaximize the benefit of antidiabetic therapies that lower blood glucose.

Intestinal Glucose Regulation Management Strategies (Scenario 3)

In the remaining management strategy, Roux-en-Y gastric bypass wasperformed resulting in improved dietary habits and a significantreduction in weight within a 3-month period. Holding all other variablesconstant (age, gender, T2D duration, smoking history, vascular diseasehistory, blood pressure, hypoglycemia, physical activity,LDL-cholesterol, HDL cholesterol, triglycerides, concomitantcardiovascular therapies) and at the same degree of HbA₁C lowering, theRoux-en-Y gastric bypass procedure was able to most effectively reducethe macrovascular event score because the dietary score (fromcarbohydrate and fat reductions), body mass index score (fromsignificant weight loss), and the Supply/Demand score (from surgicalinduced physiologic effects, primarily on the intestine) were allsignificantly reduced. In this model of macrovascular diseaseprogression in the T2D patient, the Roux-en-Y gastric bypass procedure(and also extending to other bariatric malabsorptive and restrictiveprocedures) is a non-pharmacologic example that demonstratesmacrovascular benefit beyond glucose lowering that is consistent withthe teachings of the Supply side management algorithm. The use of pHencapsulated glucose would be expected to yield results similar, but ofslightly lesser magnitude to Roux-en-Y gastric bypass because of similarphysiologic effects at the level of the intestine. Therefore, inpatients that are either unable or unwilling to undergo bariatricsurgical intervention, pH encapsulated glucose would serve as a nextbest treatment approach because it would be expected to demonstratesuperior dietary alterations, weight loss, and Supply/Demand dynamics incomparison to all other pharmacologic approaches, but in particularthose that would decrease the Supply/Demand ratio and increase thelikelihood for continuing poor dietary habits as well as hypoglycemia,weight gain, and physical inactivity (i.e. insulin and secretagogues).

Example 4

This Example provides a description and illustrative examples ofexpected results obtained by implementation of the method of theinvention under several exemplary scenarios, and in particular disclosesuse of the SD index to calculate the FS index, as well as the use of theFS index in tools for assessment, therapy, monitoring treatment, andtreatment of MS. In particular, the invention provides for determinationof the FS index of MS and its use in reducing cardiovascular riskassociated with it.

This Example provides patient specific calculations of SD and FS indicesand demonstrates the invention in terms of therapeutic approaches whichimprove or resolve metabolic syndrome and lower associatedcardiovascular risk. This Example also discloses application of theinvention in identifying drug therapies beneficial to the resolution orcontrol of metabolic syndrome, as defined by FS index measurements.

The FS index can be calculated and used in a method for determiningcardiovascular risk for an individual suspected of having, at risk for,or diagnosed with MS. The method comprises some or all of the followingsteps:

a) obtaining from an individual one or more biological parameters, andfrom the biological parameters:

b) determining the FS index, wherein the FS index is calculated as:

$\mspace{79mu} \frac{\begin{matrix}{{C{.11}\left( {\text{?} + {TG}} \right)} + {{HBA}\; 1c \times \frac{\text{?}}{4}} + {{BMI} \times \frac{\text{?}}{150}} +} \\{{{AST} \times \frac{\text{?}}{100}} + {{FB}\mspace{14mu} {insulin}\; \times \left( {{BMI}\text{-}22} \right)}}\end{matrix}}{{S/D}\mspace{14mu} {ratio}}$?indicates text missing or illegible when filed

wherein the FBG is Fasting Blood Glucose in mg/dl; the TG isTriglycerides in mg/dl; the HBA1c is hemoglobin A1c in %; the BMI isbody mass index in kg/m²; AST is Aspartate Transferase in IU/liter; FBinsulin is fasting Blood insulin concentration in nmol/liter.

The SD ratio, as explained above, is a ratio of Glucose Supply Index (S)to Insulin Demand Index (D) calculated as follows:

1+[aggregate of carbohydrate exposure(CE)+hepatic glucoseuptake(HGU)+hepatic gluconeogenesis(GNG)and+insulin resistance(IR)],

and (D) calculated as follows:

1+[aggregate of peripheral glucose uptake(PGU)+peripheral insulinexposure(PIE)].

The FS ratio results in a numerical value which can be used as an MSassessment tool in a variety of ways. For example, it is considered thatan FS index value of greater than 60 indicates that the individual is inneed of therapy for MS, or that the individual is at risk for at leastone cardiovascular complication associated with MS. As the FS indexrises above 250 the cardiovascular (CV) risk is very high, and as itfalls to values below 50, and preferably to values as low as 20, the CVrisk profile of the patient is greatly improved. In this way theinvention provides for therapeutic and/or surgical interventions thatare guided by a determination of the FS index. For instance, upondetermining an FS index value of greater than 60, the invention incertain embodiments further comprises initiating or modifying atherapeutic approach designed to address MS and/or one of its underlyingcardiovascular conditions. In one embodiment, the invention includesadministering a drug, alone or in combination with other drugs, to theindividual based on a determination of an FS index higher than 60. In apreferred embodiment the drug is pH-encapsulated glucose as described indetail in PCT/US09/005,016. This is a form of therapeutic agent whichcomprises glucose which is formed such that the glucose is released inthe intestine at jejunum or ileum at a pH at or above 7.0. Based onstudies performed to compare the potency of this formulation of pHencapsulated glucose to patients having RYGB bariatric surgery, thisformulation is considered to provide an SD value of 3.5. In anotherembodiment, the individual is treated with bariatric surgery. Bariatricsurgery is considered to provide an SD value of 4.0.

The invention also provides compositions that comprise combinations ofpH encapsulated glucose and other, additional anti-diabetes and/oranti-MS drugs. Those skilled in the art, given the benefit of thepresent disclosure, will recognize that combining such agents accordingto achieve a more desirable FS index will result in a higher SD ratio. Ahigher SD ratio accordingly results in a lowering of the FS index, whichcorrelates with improvements in cardiovascular function and a host ofother, related beneficial effects, such as prophylaxis and/or therapy ofType 2 diabetes. In certain embodiments, the drug combinations providedby the invention can have an additive, or greater than additive effecton the SD value. In certain embodiments, pH encapsulated glucose iscombined with immediate release versions of Metformin, Sitagliptin,statins, insulins, GLP-1 drugs, DPP-IV drugs, Glucokinase activators,DGAT inhibitors, SGLT2 inhibitors or combinations thereof. Theformulations can be prepared using any suitable technique, and can bedesigned to release the active ingredients simultaneously, orsequentially, and can be fast acting or time-release formulations.Additional agents that can be used in drug combinations and/orseparately administered to the patient to reduce the cardiovascular riskin metabolic syndrome include but are not necessarily limited tohormones, GLP-1 lipids, proteins, amino-acids, and other sugars orcarbohydrates, and combinations thereof.

The invention is suitable for use with individuals who have beendiagnosed with MS, or are at risk of developing MS (which can beascertained by way of the FS index) or who are being treated for MS withat least one drug which may be intended for therapy of MS or any of itsindividual manifestations. For such patients, a first FS index value canbe obtained and the patient can continue treatment with the drug for aperiod of time, during or after which a second FS index value isdetermined A lower second FS value as compared to the first FS indexvalue shows that the drug is effective for treating the MS. However, ahigher second FS value relative to the first FS index value is evidencethat the individual is in need of a change in dosing of the first drug,or is in need of a change to a different drug, or is a candidate forbariatric surgery, or combinations thereof.

In certain embodiments, the individual for which a first FS index isdetermined is an individual who is not being treated withpH-encapsulated glucose drug, and wherein a higher second SD ratio valuerelative to the first SD ratio value is determined. In such cases, theinvention provides for initiation of therapy with a pH-encapsulatedglucose drug. It will be apparent that this therapy will reduce the FSindex because when the SD ratio is increased by the addition of the drugtherapy (or by gastric bypass surgery), the SD ratio value increases,which decreases the FS index.

In certain embodiment, at the time the FS index is determined, theindividual is not being treated with a Type I diabetes drug or an MSdrug, and the SD value is 1.0. As with the SD value, the FS value can bedetermined using a computer, microprocessor, or a programmablespreadsheet, or combinations thereof.

In certain embodiments, subsequent to administration of thepH-encapsulated glucose alone or in combination with anti-diabetes drugsthat together result in a lower SD ratio, severity of one or more of thecardiovascular complications associated with MS is reduced.

The following provides a description of the development and use of theFS index.

Methods:

The FS index of MS comprises the following parameters: Fasting BloodGlucose, Fasting Insulin, HBA1c, BMI, AST, Triglycerides, GlucoseSupply-Demand (S/D) index, and in certain cases, Proinsulin. Eachparameter was mathematically arranged to increase as MS worsened, andweighted approximately equally in the prediction of MS progression andrisk for CV events. The FS index was then applied to well-studiedpatient populations described in Examples 1-3 using a neural net model.The database used to derive the FS index and test the prediction oflower cardiovascular risk profiles included 50 patients with T2D havingAMIs, 50 precisely matched T2D controls without AMIs as described above,100 patients with RYGB surgery and reversal of MS, 61 patients with T2Dgiven metformin, 12 patients on Metformin and Januvia, 33 patients onmetformin and Byetta and 18 patients given Brake therapy for HepatitisC, T2D, NAFLD, or prediabetes. FS index values were calculated fromserial laboratory and clinical data over timeframes ranging between 2and 10 years. In these patient populations, a normal FS index value isbetween 20-50. Patients with two or more manifestations of MS andincreased CV risk profiles have FS index values above 200. Maximum FSindex values are above 500, typical when nearly every MS component ishighly abnormal.

Results:

High FS index values predicted CV risk in this patient population,regardless of the specific components of MS that were abnormal. Abnormaland rising FS index values predicted AMI and other MACE events. When MSis studied as the equal weight of its components using the FS index, itis apparent why clinical strategies treating only one component of MS donot remove all risk of CV events. The index also explains why drugtherapies that improve one aspect of MS but worsen others may notmitigate CV risk or remove events. Abnormal FS index values subsequentlynormalized, indicated resolution of each component of MS syndrome,raising the possibility that specific treatments of MS might haltprogression or reverse MS entirely. For example, changes in FS index inpatients with RYGB surgery were dramatic, taking scores of thesepatients from above 250 to values below 20 in most cases. FS indexresponses to oral Brake were similar to RYGB, even though Brake treatedpatients did not lose as much weight.

There are many isolated laboratory predictors for the presence ofindividual diseases such as diabetes predicted by HBA1c or fasting bloodglucose. Such parameters predict the disease and can be used to monitorthe control of the parameter, such as when insulin lowers the bloodglucose. Laboratory predictors of diseases are designed to be applied todisease detection over very broad populations of heterogeneous patients.These patients may have complex mixtures of diseases and treatments, andthus a broadly applicable but single parameter index such as “high LDLcholesterol” might separate some of the more obvious high riskatherosclerosis patients who have underlying lipid abnormalities, butnot perform well on individuals who might deviate from the coremetabolic syndrome model used to derive the index. In the case of theubiquitous disease Type 2 diabetes, there is obvious increased CV riskon any general index because there is always an element ofatherosclerosis, but it was necessary to invent the glucose supplyinsulin demand (SD) ratio as described above when it became clear thatthe primary cause of cardiovascular events in type 2 diabetes was anoversupply of refined sugar. SD ratio defines how the sugar overdriveleads to CV risk when interacting with the diabetes drugs taken by thepatient. None of the indexes created before SD had considered theprimary cause (glucose) nor are any prior indices sensitive to theprimary means of cure (Roux-en-Y Gastric Bypass (RYGB) bariatric surgeryor ileal delivery of glucose) of Type 2 diabetes. This gap resulted inour development of SD ratio as a means of monitoring the importantaspects of type 2 diabetes. Since many of these patients have more thanone metabolic syndrome parameter abnormal, the present inventionprovides an application of the SD ratio to patients who have othermetabolic syndrome associated diseases concomitant with diabetes, suchas Congestive Heart Failure, fatty Liver disease, Hepatitis C, ChronicObstructive Pulmonary Disease (COPD), Alzheimer's disease, sepsis andothers.

Prior to the priority date of the present application, there was noindex which predicts the risk of CV events in metabolic syndrome, and infact there are still very few researchers that consider all of thesediseases to be phenotypic manifestations of an underlying metabolicsyndrome pattern of CV progression. In Examples 1-3 of the presentapplication, the S/D ratio described is for CV risk prediction inpatients with diabetes, but diabetes is just one of many end organmanifestations of metabolic syndrome, which is too narrow a viewpoint.

The presently provided FS index addresses the common manifestations ofMS, each of which has historically been thought to be variably relatedto CV endpoints. Thus, the presently provided FS index is designed tobroadly model the important aspects of metabolic syndrome (weight,triglycerides, liver inflammation, insulin production and SD ratio) andderive CV risk therefrom. There is evidence from RYGB that the presentinvention can facilitate a reversal of atherosclerosis, and surprisinglyit is expected that administration of pH encapsulated glucose will havethe same or a similar effect.

We used the FS index in conjunction with neural net models to analyze abroad population of patients for changes in CV risk using the index andclearly show that RYGB surgery (and oral pH encapsulated glucose) lowersCV risk profile of all aspects of metabolic syndrome. Thus, use of theFS index as described herein can result in all of the beneficial aspectsof RYGB surgery, and the administration of pharmaceutical compositionscomprising pH encapsulated glucose.

In one embodiment, calculation of the FS index is used to defineimprovement in patient well-being after RYGB or Brake therapy,especially from the standpoint of lowering CV risk, and the associatedimprovement in functioning of Liver, Pancreas, GI tract, Heart, Lungsand Brain. All of these areas of the body are regenerated byadministration of pH encapsulated glucose to the ileal brake.

The consensus definition of Metabolic Syndrome includes five components:Abdominal Obesity (Male>40 in waist, Female>35 inch waist), ElevatedTriglycerides(>150), Low HDL Cholesterol(<40 male, <50 female), Highblood pressure(>135/85), and Hyperglycemia (FBS>120 or HBA1c>7) (1-14).There are several other variants within the consensus definitions asmight be anticipated by a research community that does not consider thisall to have a common cause or a common treatment methodology. Use of theFS index in certain embodiments to measure each patient's baselineinsulin output and integrating the mass balance effects of diet, foodcomposition, insulin output and weight change, is a preferred method forsorting out the relative impact of diet versus the impact of RYGB onpancreatic function and insulin output in MS and T2D (and is expected tohave the same benefit on Type 1 diabetes). Since we can establish thebaseline insulin output, we can take better advantage of tools such asmetabolic syndrome indices, S/D ratios, and the present FS index, alongwith specific food component analyses (sugar, CHOs, protein, etc). Theend result of these parameters and biomarkers is control of metabolicsyndrome, and we are now able to determine the novel impact of theinvented indexes on the risk of CV disease.

Without intending to be constrained by theory, it is considered thatmetabolic syndrome leads to an end organ manifestation such as T2D via acontinual supply of immediately available carbohydrates, which drivesexcessive output of the pancreatic beta cells. Glucose supply drivenpancreatic stress in absence of ileal hormone signaled pancreatic repairleads to pancreatic exhaustion, acceleration of insulin resistance, Type2 diabetes, NAFLD and obesity, all of which are core end-organmanifestations of Glucose Supply Side driven metabolic syndrome.

Occurring in parallel, but heretofore undiscovered, the patient underaccelerating glucose load and pancreatic stress has a progressive lossof hormone mediated pancreatic, Liver, GI and other organ repair andregeneration capabilities. The pace of metabolic syndrome damageincreases as endogenous repair and regenerate pathways shut down underthe increasing load of rapidly absorbed sugars and other refined foods.

Again without intending to be bound by any particular theory, it isconsidered that glucose, hunger and MS all relate to continued hunger asthe driver of glucose supply side driven MS, and there are acceleratinghunger signals emitted from the L-cells of the ileum in absence ofsufficient supply of distally available carbohydrate to satisfy theneeds of the bacterial flora in the ileum, and in fact the L-cellsthemselves. Alterations of intestinal microflora numbers and species,and their need for nutrition continues to signal hunger to the host byuse of L-cell signaling procedures to turn off satiety signals, with thecore driver being the demand for nutrition by the organisms. In theabsence of carbohydrates and certain lipids at the level of the ileum,the host and host bacterial signal is for continued hunger; the combinedsignal from absence of these substances in the presence of hungryorganisms suppress the ileal hormone output from the L-cells. As thehuman consumes more and more nutrition, eventually the resulting hostover-nutrition spills over to the ileum, removing the bacterialsuppression of the ileal hormones and allowing a satiety signal untilabsence begins the cycle again. In certain conditions such asmalabsorption or RYGB surgery, excessive carbohydrates arrive at theileum and in this case, the signals completely over-ride hunger. IlealHormone outputs not only produce satiety but also begin to trigger theendogenous repair of pancreas, liver and GI tract cells. Together, theseare the novel “stop and repair” processes that are programmed into ourbodies to optimize the balance between ingestion and nutritional needs.As these systems are mainly programmed to satisfy basic needs fornutrition, they are most efficient in a relative lack of glucose asnutrition. Our current excessive nutrient ingestion patterns, especiallya growing preference for rapidly absorbed immediate release andduodenally absorbed sugars that deny nutrition to distal intestinalbacteria, create an overdrive of the hunger pathways directly to organexhaustion and obesity without the benefit of the triggered repair. Asimple fix for over-nutrition with rapidly absorbed sugars is to performRYGB surgery. However, it is less invasive to provide oral formulationsof carbohydrates that are directly released at the L-cells in a dosesufficient to trigger the stop and repair processes that protect us fromaccelerations in metabolic syndrome and obesity. In concert with this,the present invention provides pharmaceutical compositions and methodsof regenerating organs and tissues in a patient afflicted with one ormore organ or tissue manifestations of glucose supply side associatedmetabolic syndrome, when the syndrome is accompanied by suppressedregenerating processes and progressively failing organs. Apharmaceutical composition in an effective dosage is provided to an MSpatient, which wakes up the dormant ileal brake sensor and initiatesrenewed hormonal signals to regenerate candidate organs and tissuesincluding but not limited to the pancreas, the liver, the enterocytes ofthe GI tract and the associated signal transmitting neurons. Theseactions are controlled by measured biomarkers of both the ileal hormoneprocess and the resolution of metabolic syndrome and organ repair. Byway of example, directly regenerating pancreas, liver andgastrointestinal tract functions are specifically described herein andattributed to treatment with a specific pharmaceutical composition.

Serially Measured biomarkers of metabolic syndrome progression orregression, in this case the components of the FS index in patientsgiven Brake or taken to RYGB surgery, demonstrate the successfulregeneration of organs, t Once regeneration is accomplished by Brake orRYGB surgery, the regenerated organs then signal the patient, to resumeadequate nutrition seeking behavior as directed by restored signals ofhunger. Specific actions on organ regeneration are confirmed by measuredbiomarkers and analysis of the results. Dependent on reservecapabilities of the patient at hand, and depending on composition andadministered dosage of the pharmaceutical composition, the presentinvention relates to dramatic improvement or potential cure of metabolicsyndrome manifestations including but not limited to diabetes,hyperlipidemia, atherosclerosis, insulin resistance, hypertension, andobesity.

Specific actions on organ regeneration are confirmed at each stage oftreatment by measured biomarkers and FS index calculation, and analysisof the results and use of the index to adjust dosage and duration oftreatment with the pharmaceutical composition. Dependent on reservecapabilities of the patient at hand, and depending on composition andadministered dosage of the pharmaceutical composition, the presentinvention relates to dramatic improvement or potential cure of metabolicsyndrome manifestations including but not limited to diabetes,hyperlipidemia, atherosclerosis, insulin resistance, hypertension, andobesity. The FS index demonstrates these effects of said composition tothe treating physician and thereby provides a roadmap to theregeneration of organs and tissues and associated lowering ofcardiovascular risk.

Graphics Methodology and Display of Parameters Except where specificallynoted, standard deviations for each parameter are displayed on the Yaxis, as this factor normalizes the different range of parameters forvisual illustration of behavior patterns in groups on a common Y axis.Unless otherwise noted, the X-axis displays time throughout this report.

Neural Net Models and Data Display Methods. The initial association ofbiomarker-mortality response surface was confirmed and extended byperforming subset analyses to identify the most informative inputbiomarkers on cardiovascular events and outcomes. Throughout thisdescription of the invention, raw data from each patient are displayedvs. time, and cumulative graphics are either displayed or otherwise inour possession. Unless otherwise stated, standard deviations (z-score)vs. time are presented in the individual and mean population graphics.

For purposes of analysis, clinical and laboratory parameters wereconverted into modified z-scores as follows:

-   -   A mean normal value (described as “mean” in the following) was        selected based on review of literature and various published        laboratory compendia. The Standard Deviation (SD) of each        parameter was set to one half of the normal range. The modified        z-score is calculated as follows:

z=(Patient value−“mean”)/SD

-   -   On the graphs, the z-score is reported as the number of SD.

For data discussed below, the “mean” and standard deviations areprovided for selected parameters.

Laboratory Biomarkers

Laboratory blood tests collected serially on the FS index modeldevelopment population included the following:

-   -   Complete Blood Count (Hemoglobin, Hematocrit, WBC, differential,        platelet count)    -   Serum Chemistry (Na, K, Cl, glucose, Calcium, BUN, Creatinine,        ALT, AST, Alkaline Phosphatase, Lactate Dehydrogenase, total        bilirubin)    -   Coagulation: PT, INR, fibrinogen, platelet count

For each study patient, we had complete access to all raw data, measuredvital signs, culture results, and clinician assessments. Many of thesemeasures were incorporated as inputs into the neural net models, and arealso illustrated on the Y axis as standard deviations above the definednormal mean of the parameter. The primary aim of the neural net modelingeffort was to model CV events and CV mortality in relation to timecourse of Input parameters, with a second primary effort to model timecourse of organ failure as a metric in relation to Input factors such asthose in the laboratory biomarkers listed above.

Concomitant Diabetes in metabolic syndrome patients: S/D ratio and CVevents

Examples 1-3 present a T2D disease progression model that characterizesthe effect of conventional antidiabetic therapies on the glucose supplyand insulin demand dynamic that defines metabolic syndrome associatedT2D, and links this S/D index to cardiovascular risk specific to thetreatment of T2D patients.

For microvascular outcomes, there is compelling historical andcontemporary evidence for intensive blood glucose reduction in patientswith either T1D or T2D. There is also strong evidence to supportmacrovascular benefit with intensive blood glucose reduction in T1D.Similar evidence remains elusive for T2D. Because cardiovascular outcometrials utilizing conventional algorithms to attain intensive bloodglucose reduction have not demonstrated superiority to less aggressiveblood glucose reduction, it should be considered that the means by whichthe blood glucose is reduced may be as important as the actual bloodglucose.

SD ratio derivation, testing methods: By identifying quantitativedifferences between antidiabetic agents on carbohydrate exposure (CE),hepatic glucose uptake (HGU), hepatic gluconeogenesis (GNG), insulinresistance (IR), peripheral glucose uptake (PGU), and peripheral insulinexposure (PIE), we created a pharmacokinetic/pharmacodynamic model tocharacterize the effect of the agents on the glucose supply and insulindemand dynamic. Glucose supply was defined as the cumulative percentagedecrease in CE, increase in HGU, decrease in GNG, and decrease in IR,while insulin demand was defined as the cumulative percentage increasein PIE and PGU. With the glucose supply and insulin demand effects ofeach antidiabetic agent summated, the glucose supply (numerator) wasdivided by the insulin demand (denominator) to create a valuerepresentative of the glucose supply and insulin demand dynamic (SDratio). Alpha-glucosidase inhibitors (1.25), metformin (2.20), andthiazolidinediones (TZDs; 1.25-1.32) demonstrate a greater effect onglucose supply (SD ratio>1), while secretagogues (0.69-0.81), basalinsulin (0.77-0.79), and bolus insulin (0.62-0.67) demonstrate a greatereffect on insulin demand (SD ratio<1). From these studies we concludethat Alpha-glucosidase inhibitors, metformin, and TZDs demonstrate agreater effect on glucose supply, while secretagogues, basal insulin,and bolus insulin demonstrate a greater effect on insulin demand.Because T2D cardiovascular outcome trials have not demonstratedmacrovascular benefit with more aggressive blood glucose reduction whenusing conventional algorithms that predominantly focus on insulindemand, it would appear logical to consider a model that incorporatesboth the extent of blood glucose lowering as HBA1c and the means bywhich the blood glucose was reduced (SD ratio) when consideringmacrovascular outcomes.

It was our objective to test the hypothesis that, in conjunction withHBA1c, patients managed on the glucose supply side of the model wouldhave fewer CV events versus those managed on the insulin demand side. Totest this hypothesis, the electronic medical records of a group modelhealth maintenance organization were queried to compile a population ofpatients meeting the following inclusion criteria: (1) type 2 diabetesmellitus T2D, (2) known date of T2D diagnosis; (3) ICD-9 or CPT codeidentification and chart review confirmation of a first majorcardiovascular event (myocardial infarction, coronary artery bypassgraft, or angioplasty), (4) five years of continuous eligibility, and(5) on antidiabetic therapy at the beginning of the 5-year observationperiod. These patients were subsequently matched (1:1) to T2D patientsmeeting the same criteria who had not experienced an event and wereanalyzed for differences in glucose control (HbA1C), the glucosesupply:insulin demand dynamic (SD ratio), and categorical combinationsof both parameters. RESULTS: Fifty cardiovascular event patients metinclusion criteria and were matched to controls. No difference wasobserved for the average HbA1c or SD ratio between patients experiencingan event and controls (7.5+/−1.0% versus 7.3+/−0.9%, p=0.275, and1.2+/−0.3 versus 1.3+/−0.3, p=0.205, respectively). Likewise, forcategorical representations, there were no differences in event rate atthe pre-identified breakpoints (HbA1c>or=7% versus<7%; 72% versus 64%,p=0.391, and SD ratio>or=1 versus<1; 68% versus 76%, p=0.373, >or=1.25versus<1.25; 42% versus 56%, p=0.161, >or=1.5 versus<1.5; 22% versus30%, p=0.362, respectively). Analyzing the combined effect of glucosecontrol and the SD dynamic, patients managed at higher glucose valuesand on the insulin demand side of the model (HbA1c>or=7% and SDratio<1.25) tended to have greater cardiovascular risk than thosemanaged at an HbA1c<7%, or HbA1c>or=7% with an SD ratio>or=1.25 (61%versus 39%; p=0.096). Independently, more aggressive HBA1c reduction andhigher SD ratio values were not independently associated with areduction in cardiovascular outcomes. Combining the parameters, it wouldappear that patients managed at higher glucose values and on the insulindemand side of the model may have increased cardiovascular risk.

FS Index of Metabolic Syndrome: Risk Factors for Cardiovascular Events

We tested the hypothesis that, in conjunction with HBA1c, patientsmanaged on the glucose supply side of the model would have fewer CVevents versus those managed on the insulin demand side. As a test ofthis hypothesis, the electronic medical records of a group model healthmaintenance organization were queried to compile a population ofpatients meeting the following inclusion criteria: (1) T2D, (2) knowndate of T2D diagnosis; (3) ICD-9 or CPT code identification and chartreview confirmation of a first major cardiovascular event (myocardialinfarction, coronary artery bypass graft, or angioplasty), (4) fiveyears of continuous eligibility and serial monitoring of FS indexcomponent parameters, and (5) on antidiabetic therapy at the beginningof the 5-year observation period. These patients were subsequentlymatched (1:1) to T2D patients meeting the same criteria who had notexperienced an event and were analyzed for differences in glucosecontrol (using HBA1C), the glucose supply:insulin demand dynamic (usingSD ratio), and categorical combinations of both parameters. Fiftycardiovascular event patients met inclusion criteria and were matched tocontrols. No difference was observed for the average HBA1c or SD ratiobetween patients experiencing an event and controls (7.5+/−1.0% versus7.3+/−0.9%, p=0.275, and 1.2+/−0.3 versus 1.3+/−0.3, p=0.205,respectively). Analyzing the combined effect of glucose control and theSD dynamic, patients managed at higher glucose values and on the insulindemand side of the model (HBA1c>or=7% and SD ratio<1.25) tended to havegreater cardiovascular risk than those managed at an HBA1c<7%, orHBA1c>or=7% with an SD ratio>or=1.25 (61% versus 39%; p=0.096).Independently, more aggressive HBA1c reduction and higher SD ratiovalues were not independently associated with a reduction incardiovascular outcomes. Combining the parameters, it would appear thatpatients managed at higher glucose values and on the insulin demand sideof the model may have increased cardiovascular risk.(15, 16)

We have extended this concept from the T2D-centric HBA1c-SD parameterdescribed in Examples 1-3 to create the FS index as a global index ofmetabolic syndrome, thus providing a quantitative means of describingprogression of MS in Patients. The FS index is meant in certainembodiments to track the beneficial changes in metabolic syndrome as itis managed by RYGB or by Brake, in turn a link to measurement ofregeneration in the systems affected by the underlying common metabolicsyndrome of these patients.

As underlying Metabolic Syndrome has many different manifestations inaddition to those considered reflective of T2D, the FS index includeshyperlipidemia, obesity and NAFLD, in order to facilitate trackingprogression of MS in patient populations that may have any or all ofthese conditions to varying degree. As one non-limiting example of theutility of why FS, it is known that antidiabetic drugs lower glucose butraise lipids or BP, and thus the net effect is to worsen the MetabolicSyndrome and increase CV risk. It was our hypothesis that improved riskscoring could be accomplished via an index that considered a compositeof Metabolic Syndrome system components.

The FS index considered the following: Fasting Blood Glucose, FastingInsulin, HBA1c, BMI, AST, Triglycerides, Glucose Supply-Demand (S/D)index, and in some cases Proinsulin. As proinsulin was not available tous in datasets used herein, the index was used without considering thesedata. Each of these parameters was mathematically arranged to increaseas Metabolic Syndrome worsened, and weighted approximately equally inthe prediction of Metabolic Syndrome progression and risk for CV events.

The FS index was then applied to well-studied patient populations usinga neural net model.

Patient Populations

The study is a comprehensive analysis of a large managed care populationof metabolic syndrome patients, wherein we have obtained use of longterm data from 194,000 patients that have been followed for up to 20years. The database contains a large amount of information per patientthat is routinely collected in clinical practice and hospitals. We alsohave complete electronic medical records of 100,000 patients who arehospital inpatients in Western NY, and these cases sometimes include thecomplete hospitalization records of the Managed care population withmetabolic syndrome. Throughout these electronic records, we have refillsof each prescription coded to NDC number, so all patients can be trackedfor adherence to therapy on a long term basis. It is important to pointout that the data had to be extracted and tabulated to be specific tothe purposes of this study, and that require f data analyst time andconsiderable amounts of medical review. Thus the first study task was tocreate a metabolic syndrome subset from the available electronic medicalrecords of all these patients.

Each metabolic syndrome patient and applicable controls wascharacterized in relationship to all treatments and pharmaceuticalsgiven over the entire period of monitoring. Each patient also hadadequate records to characterize long term progression of the disease,and a complete record of potential cardiovascular events that can bestudied against exposure in a classical time and event progressionmodel. Each patient is examined both separately and as part of subgroupsby exposure against each relevant endpoint.

The data analysis leading to an index useful for defining CV eventswithin a patient population with varying degrees of underlying metabolicsyndrome becomes a database assembly task and then a comprehensiveanalysis and mathematical modeling of a large managed care population ofmetabolic syndrome patients. The data assembly task consists ofintegration of the data in SQL and then porting the records to MatLab,which is how all of the data in our large Electronic Medical Recordsdatabase of metabolic syndrome patients is treated after completede-identification. The end result is common fields and meaningfulendpoints for analysis and modeling across the entire dataset.

Structure of Longitudinal data, interpolations, carry-forwards.

To enable analysis of disparate data (laboratory and clinical) that isnot necessarily complete nor synchronous, discrete measurements of eachinput and output parameter were used to populate continuous longitudinal“waveforms” with a time resolution of 30-90 days. Measurements wereplaced in the longitudinal “waveform” at the time point corresponding tothe date closest the date of the actual measurement (which might be upto 15 days before or after the actual measurement date). If the timedelay between subsequent observations was more than 45 days, a 3 stepinterpolation was used. To generate the time ramp the initial value wasused for the first ⅓ of the period between the two observations, thesecond value for the final ⅓, and a linear interpolation linked thosetwo values. Values prior to the first observation and after the lastobservation were set to 0 (normal).

Load and Load Metric

Load metric was calculated for known toxins such as cigarettes as, forexample the primary driver of lung injury in a load model with anendpoint of COPD, and for FS index as the primary driver of CV and MACEevents in metabolic syndrome patients with CV endpoints. Each componentparameter of Load was normalized by subtracting the population mean anddividing by a population standard deviation. These normalized metricswere then summated to calculate the Load Metric.

Organ Failure Metric—Example of Component Organ Definitions Used inModels

Organ Failure Metric (OrganMetric or OM) is the primary calculatedparameter used to operate the time related progression model withrespect to evolution of organ failures. OrganMetric is created by addingtogether z-scores for individual organ failures; details of this processare listed for each component of the metric in the descriptions below.Higher scores reflect increasing abnormality. Therefore, when anabnormality in a parameter is indicated by negative value, its z-scoreis subtracted from the OrganMetric to reflect increase in abnormality.

Liver:

Bilirubin total from local laboratory was the primary measure of hepaticfunction if available, otherwise bilirubin from central lab ifavailable. Normalized (mean 0.7, SD 2.0) and used only values above 0,any values below 0 (normal) were set to 0.

Kidney:

Serum creatinine was the primary measure of renal function. Normalized(mean 0.8, SD 1.0) and used only values above 0, any values below 0(normal) were set to 0.

Lungs:

Pulmonary abnormalities reflect defects in oxygenation. “LungProduct”was used if available. LungProduct is defined as (PaO2/FI02)/respiratoryrate. If ventilator rate was available, it was used instead ofrespiratory rate. LungProduct was first calculated using actual valuesof respiratory rate, FIO2 and/or PaO2 and then normalized (mean 25, SD5). A negative value of LungProduct reflects pathology; values>0 wereset to 0 and the value of LungProduct was subtracted from the compositeOrganMetric.

Platelets:

Normalized actual platelet count based on normal values (mean 300, SD150). In metabolic syndrome the primary platelet abnormality isthrombocytopenia. Therefore the value of this parameter was subtractedwhen calculating composite OrganMetric.

Blood pressure and Hypotension:

The database contains numerous systolic and diastolic blood pressurereadings, which were displayed as standard deviations from normalvalues.

Input/Output Considerations—Organ Metric as Representative of EachMetric Used

The OrganMetric was then calculated for every recorded time period usingthe component parts as described above. Normalization for the number ofcomponents was done based on the components that were present at anytime in the data set, so as to provide an average metric over theparameters that were available for each patient.

If the “sum” or AUC of the OrganMetric was being used as an input, thehourly components were added for the full duration of the data set. Inthis manner a patient who had an average organ metric of 2 for 2 days,would have the same AUC as a patient who had an average organ metric of1 for 4 days.

At the final integration stage of the modeling, the score was normalizedby the number of data values that were available from each patient. Thefinal OrganMetric for a patient was the average metric over all organswith available data.

A neural net approach defines a modeling system that can stratifymetabolic syndrome into patterns of response, and by so doing canidentify an enriched population of individuals who will be specificallyresponsive to metabolic syndrome therapies based on upon theirinteractions with biomarkers and drug exposure.

A neural net was used to perform a multiple parameter logisticregression while allowing for non-linear (usually sigmoidal) dependenceof responses on input parameters. Candidate input parameters are firstused individually to model the desired output and are then ranked basedon the error in these single parameter models. A small number (usually 2or 3 in this project) of the best input parameters are then chosen andused to create the final multi-parametric model which yields a lowermodeling error than the original single parameter models.

Model Design

Structural Modeling of complex biological events like metabolic syndromeprogression may be problematic due to the incompleteness of theavailable knowledge about the underlying mechanisms and to the lack ofan adequate observational data set. As a novel aspect of the presentinvention, non-linear approaches to input-output systems such as neuralnetworks offer a valid alternative to classical structural modeling insettings with sparse data. In practice, classical statistical methods,which assume linear dynamics of the system from input-output data, failwhen the experimental data set is poor either in size or quality, or thesystem to be modeled is non-linear or when input-output relationshipsare offset in time. In the case of metabolic syndrome progression toMACE events, there are many clearly evident nonlinear and time dependentinput-output processes. Therefore to overcome this fundamental datamanagement problem, a neural net was used to link input data to desiredoutputs while allowing for non-linear and time dependent behavior inboth input and output functions.

The modeling approach used to produce the present invention is a hybridapproach intended to overcome the problems listed above. The metabolicsyndrome variables were modeled with neural networks using MatLab overthe time-course of metabolic syndrome progression. All enrolled caseswere initially considered candidates for modeling but in some casesthere were insufficient data for some parameters to be useful.

Neural networks were used to select informative patient behaviorpatterns from the overall study population of metabolic syndromepatients followed over a sufficient time-period to identify the linkbetween biomarkers and efficacy of metabolic syndrome treatments in alarge fraction of the study population. The enriched population wasfound by using the neural net to rank order inputs candidates for agiven output, the primary output being mortality as affected bytreatment (drug or placebo).

Sets (typically two or three) of highly ranked input parameters wherethen chosen to model the CV events and progression of CV events output.Once a pair of input parameters was chosen, a new neural net model wasformed to predict mortality based on this input pair. All analysis andmodeling was performed in MatLab.

The neural net model looks for subtle differences in correlationsbetween input parameters and endpoints based on longitudinal data, andcan find relationships that are non-linear or that only occur forpatients with a certain pattern of parameters. When we have a populationof patients (some treated and some not) with a varying degree of drugresponse, the neural net model was used to identify subpopulationsenriched for a response parameter to a drug or a risk parameter from adrug or its absence. We call this process enrichment and it is a novelapproach to the analysis of pharmaceutical data that allows greaterlearning from smaller subsets of patients, amply justifying the size ofthe database used to develop the FS index and used herein The neural netclearly identifies different response patterns in the cardiovascularrisk profile of metabolic syndrome patients.

Because this is a case series and the outcomes are time relatedendpoints linked to exposure parameters, the statistical analysisapproach is based on subgoups (cells) formed from drug exposurecategories by actual prescription refill records over time. Thus thedata are adjusted in the beginning for patient compliance.

Statistical Plan

Classical statistical analysis of longitudinal data such as the mergeddatabases would employ Kaplan-Meier plots of time vs. a survivalfunction produced from categorical events. Although many of theseendpoints of interest in our patients are categorical events, bycontrast our analysis methodology placed its emphasis on time to eventsand most importantly the linkages between cumulative treatment exposureover the disease progression and the time in the disease course thatevents occur. This approach strengthens the impact of associationsbetween variables, because it converts events to dose response metricswhich are inherently more robust in progression modeling. Independentvariables become absolute and time related quantitative exposure to atreatment can be precisely measured if background progression in itsabsence is precisely characterized. This “cumulative exposure per unitof time” approach is better for characterizing disease progression ratesand response to timely interventions that are often mixed with times ofdrug free progression of the disease.

Within each subgroup, there will be sufficient power to assess theimpact of cumulative exposure to glucose modified by SD ratio drugs oneach of the primary time related endpoints, but in common withstatistically driven meta-analysis projects of similar size, thisanalysis may not be sufficiently powered to assess exposure subgroupdifferences in overall mortality, even though there were many promisingsignals with respect to CV events. The same viewpoint is likely true foreven attributable mortality, although a robust definition ofattributable mortality does improve these types of correlation.Nevertheless, evaluation of these outcomes data generally consistsprimarily of summary statistics, specifically, estimation of responserates and 95% confidence intervals.

Modifying variables in these analyses are not pre-specified, rather thedata are analyzed to derive informative covariates.

Within each exposure cell, a parallel statistical approach is employed,including a multivariate logistic regression modelcreated to determinewhich co-factors were associated with progression of diabetes toendpoints such as numbers of CV events, mortality and time to adverseevents linked to drug therapies. The pool of candidate regressors in thecompanion statistical models include patient demographics, prior medicalconditions, impact of smoking history as pack years, and impact ofenvironmental exposures in the work environment, available biomarkers ofinflammation and infection, any prior antibiotic within 90 days. Sincethe number of candidate regressors in these statistical models are toolarge even within a relatively large database, the final multivariatestatistical model is configured to consider only those variables withp<0.25 in an initial bivariate analysis. The Neural Net MatLab model, onthe other hand, carries all factors forward without forming subgroups,and defines their relative importance to the endpoints.

Statistical analyses of the data within multiple logistic regressionmodels were then used to link efficacy and biomarkers as populationvariables to selected outcomes in the informative sub-populations (i.e.the population selected by the enrichment algorithm).

Descriptive statistics were only applied to the data in the tables,primarily using chi square calculations. Demographics were compared withchi-square/Fischer's Exact Test for categorical items and 2-way ANOVAfor continuous variables. Clinical response was assessed bychi-square/Fischer's Exact Test.

All statistical tests were run as two-sided with the probability of aType I error p<0.05 considered statistically significant.

Approach to Display of Progression Modeling Results

Outputs of the many runs of the database thru the Neural Net models arepresented herein, both in graphical format and tables. In general, weuse graphical displays for individual patients and groups of similarpatients, and we use tables to present the results of runs of aggregateanalyses performed on the individual patients. The general theme ofpresenting some highlights of the results is outlined as follows:

Input/Output Plots for:

-   -   Groups of patients    -   Subsets of metabolic syndrome patients with common        characteristics    -   Individual patients with top 10 informative parameters displayed        over time

In each Input/Output graphic, the x axis is time and the y axis ismultiples of SD over the normal value which is set at zero. This allowsall parameters approximately equal weight in the display, recognizingthat parameters behaving in a non-linear fashion will always appear moreimportant in terms of large changes and that display bias cannot becompletely removed from the display.

Ranked Correlation Lists for:

-   -   diabetes events    -   CV events    -   Pharmacoeconomic Analyses    -   Drug impacts on metabolic syndrome endpoints

Tables of rank ordered correlation parameters provided herein are allbased on the somewhat time independent link between Inputs (usually thebaseline parameter value at time of metabolic syndrome diagnosis) andOutputs calculated as cumulative or AUC variables; the multiples herestated are used to rank order the input in connection to the magnitudeof the output, connecting inputs and outputs regardless of timing.Output Error is the Root Mean Squared (RMS) error between the enrichmentmodel based on the input parameter (in this case the baseline biomarker)and the desired output of (for example, CV risk or COPD progression byGLG score), based on each input parameter for all the patients. A loweroutput error means that the parameter on its own is a better predictor,and the model seeks to find the best single parameter in all cases ofRMS rank ordering.

3D Displays Applied to Ranked Correlation Parameters

These displays generally use the top two parameters for a rankedcorrelation and display them in 3D against a Z axis parameter of definedimportance, such as cumulative CV events or cumulative organ failures,etc. In some settings we use a parameter of interest even when it doesnot achieve “top 2 status” in ranked correlation, simply because itallows the study of the parameter more specifically across the entirepopulation

Ordered correlations of Z-axis displays, which position each patientsrank and calculate magnitude of differences over null.

These two dimensional graphical displays order the x-axis to start withthe patient of lowest risk at zero, and the patient of highest risk atthe last value. The y-axis is the risk score itself. Then we use colorto define which patients have the event in question. For example, weshow increasing risk for CV events on these graphs, and then apply amarking symbol to identify the patients with the actual events vs theirrisk in an easy to identify display. Calculations of relative risk overzero (the point separating half above and half below) allows an overallestimate of increasing or decreasing probability that roughly followsthe more widely applied odds ratio. The advantage of doing the analysiswith a neural net is that non-linear behavior is not excessivelyweighted over linear behavior

Tables aggregating behavior and identifying subsets for EnrichmentStudies

Final tables aggregating patterns of population behavior are derivedfrom analysis of each individual, once again rank ordering inputs tooutputs. In this run of the neural net, the task is to identify the top2-4 inputs for the particular behavior pattern of interest, such ascardiovascular events as used herein to develop the FS index. Thetabulation of these data elements, rank ordered, are then used to definesubsets that might be a focus for enrichment studies

Summary of Modeling Results(Figures Table Text in the Examples Beyond00200)

In the art of medicine, physicians consider each of the various aspectsof metabolic syndrome to be a single disease, and they use a single labtest to diagnose or monitor treatment progress. An example would be theuse of BMI to diagnose or monitor obesity, HBA1c or glucose to diagnoseor monitor diabetes, or cholesterol to diagnose or monitorhyperlipidemia. None of these approaches consider direct effects ofpharmaceutical treatments, which themselves change the CV risk withineach disease, as well as overall. None of these indices considerrelative importance if all the aspects are present within each patient,as usually occurs in the art of medicine. None of these single lab testparameters is a useful predictor of cardiovascular events. As we soughtan index that would apply to metabolic syndrome and predict thecardiovascular risk therefrom, we initially worked on the diabetespredictor, and that lead to the discovery of the SD ratio, whichincorporates a glucose driven approach to diabetes and a CV risk drivenpartly by how the various treatments alter CV risk within the glucosesupply and insulin demand pathway to CV events. On this basis, we wereable to improve the art of selecting better drug treatments for diabetesand for the first time provided a novel explanation why RYGB surgery isa better means of lowering CV risk in T2D over any current drug therapy,which is disclosed in Examples 1-3. It is notable that the superioroutcomes of bariatric surgery over drug treatment in type 2 diabeteshave been published recently, but the authors did not provide anyexplanation or the mechanism of the superior effects in this paper. BothSD ratio and FS index were invented to explain this mechanistic pathway,and of necessity to invent a new treatment for type 2 diabetes, which ispH encapsulated glucose. As we further examined patients and their CVrisk, we then explained diabetes as one aspect of an underlyingmetabolic syndrome but then realized that we needed to consider all ofthe other components of metabolic syndrome to fully explain CV risk inpatients. Patients with one component of metabolic syndrome such as T2Dusually have elements of additional components, such as NAFLD, elevatedtriglycerides, hypertension and others, each of which clearly impactstheir CV risk profile. There was heretofore no available index ofmetabolic syndrome that links each element to cardiovascular risk. Thusit was necessary to invent a metabolic syndrome index which waspredictive of CV events overall in patients who had elements of morethan one individual component of metabolic syndrome. Thus, we haveextended the discovery of CV risk prediction based on the SD ratiobeyond the DIABETES-centric HBA1c-SD parameter as shown in Examples 1-3to create a global index of metabolic syndrome, i.e., the FS index, toprovide a quantitative means of describing progression of MetabolicSyndrome in Patients. The FS index has been designed as a means ofidentifying patients whose CV risk is elevated by any or all componentsof metabolic syndrome, and measuring benefit when their metabolicsyndrome it is managed by RYGB or by the oral RYGB mimetic drug Brake.These latter two approaches to putative cure of metabolic syndrome arethus far the only means of definitively lowering CV risk in patientswith one or more “disease” manifestations of their underlying metabolicsyndrome. Without intending to be bound by theory, it is considered thatthe combined teaching of FS index, RYGB and Brake is that there is oneprimary cause of all of the manifested disease components that aredefined as metabolic syndrome, and there is one general approach tolowering this risk and resolving the individual components, whichactivation of the ileal brake directly via surgery, or orally withBrake, the target defined ileal hormone releasing substance.

As underlying Metabolic Syndrome has many different manifestations inaddition to those considered reflective of T2D, the FS index includedhyperlipidemia, obesity and NAFLD, in order to facilitate trackingprogression of Metabolic Syndrome in patient populations that may haveany or all of these conditions to varying degree. We now use tests foreach component of Metabolic Syndrome. As one non-limiting examples ofwhy FS index is meaningful, it is known that antidiabetic drugs lowerglucose but raise lipids or BP, and thus the net effect is to worsen theMetabolic Syndrome and increase CV risk. It was our hypothesis thatimproved risk scoring could be accomplished via an index that considereda composite of Metabolic Syndrome system components. This lead to thediscovery of the beneficial effects of an oral ileal brake hormonereleasing substance.

Specific Example A

FS Index as a measure of treatment response to Brake treatment ofMetabolic Syndrome.

In particular, the present invention generally proceeds when the stepsin practice of the invention include the testing the patient forlaboratory biomarker patterns, use of the results of testing tocalculate the FS index, determining the risk of CV events from the FSindex calculation, then personalized treatment to lower the FS index,most preferably by the administration of a pharmaceutical compositiontargeted to a specific receptor cell in the distal intestine, in adosage and duration of treatment to lower the FS index of the patientupon repeat measurements. The effect of the medicament on the measuredbiomarkers demonstrates beneficial properties of the ileal brake hormonereleasing substance on the laboratory tests that comprise the FS index.In the ordinary assessment of the precise sequence of hormonallyproduced events, the patient experiences cessation of hunger. Withrespect to the sequence of signaling molecules from the ileum, aresponse to the medicament, there is a wake up stimulation of distalintestinal L-cells that have been quieted by actions of intestinalbacteria or metabolic disease; there is a release of hormones andsignals from said L-cells; said released hormones traveling in portalblood to pancreas, liver and GI tract, said organs regenerated fromavailable growth factors and hormone signals, measured biomarkers of theFS index demonstrating the successful regeneration and said regeneratedorgans then signaling the patient, preferably a human, to resumeadequate nutrition seeking behavior as directed by restored signals ofhunger.

In Examples 1-3 we presented a T2D disease progression model thatcharacterizes the effect of conventional antidiabetic therapies on theglucose supply and insulin demand dynamic that defines metabolicsyndrome associated T2D, and links this S/D index to cardiovascular riskspecific to the treatment of T2D patients. In this Example, we testedthe hypothesis that, in conjunction with HBA1c, patients managed on theglucose supply side of the model would have fewer cardiovascular eventsversus those managed on the insulin demand side. As a test of thishypothesis, the electronic medical records of a group model healthmaintenance organization were queried to compile a population ofpatients meeting the following inclusion criteria: (1) T2D, (2) knowndate of T2D diagnosis; (3) ICD-9 or CPT code identification and chartreview confirmation of a first major cardiovascular event (myocardialinfarction, coronary artery bypass graft, or angioplasty), (4) fiveyears of continuous eligibility and serial monitoring of FS indexcomponent parameters, and (5) on antidiabetic therapy at the beginningof the 5-year observation period. These patients were subsequentlymatched (1:1) to T2D patients meeting the same criteria who had notexperienced an event and were analyzed for differences in glucosecontrol (using HBA1C), the glucose supply:insulin demand dynamic (usingSD ratio), and categorical combinations of both parameters. Fiftycardiovascular event patients met inclusion criteria and were matched tocontrols. No difference was observed for the average HBA1c or SD ratiobetween patients experiencing an event and controls (7.5+/−1.0% versus7.3+/−0.9%, p=0.275, and 1.2+/−0.3 versus 1.3+/−0.3, p=0.205,respectively). Analyzing the combined effect of glucose control and theSD dynamic, patients managed at higher glucose values and on the insulindemand side of the model (HBA1c>or=7% and SD ratio<1.25) tended to havegreater cardiovascular risk than those managed at an HBA1c<7%, orHBA1c>or=7% with an SD ratio>or=1.25 (61% versus 39%; p=0.096).Independently, more aggressive HBA1c reduction and higher SD ratiovalues were not independently associated with a reduction incardiovascular outcomes. Combining the parameters indicates thatpatients managed at higher glucose values and on the insulin demand sideof the model may have increased cardiovascular risk.

As discussed above, the FS index measures the following: Fasting BloodGlucose, Fasting Insulin, HBA1c, BMI, AST, Triglycerides, GlucoseSupply-Demand (S/D) index, and Proinsulin. Each of these parameters wasmathematically arranged to increase as Metabolic Syndrome worsened, andweighted approximately equally in the prediction of Metabolic Syndromeprogression and risk for CV events.

FS Index Equation:

$\mspace{79mu} \frac{\begin{matrix}{\text{?}\left( {{FBG} + {TG} + {{HBA}\; 1c \times \frac{\text{?}}{4}} + {{BMI} \times \frac{\text{?}}{\text{?}}} +} \right.} \\{{{AST} \times \frac{{TG} \times 4}{100}} + {{FB}\mspace{14mu} {insulin} \times \left( {{BMI} - 22} \right)}}\end{matrix}}{{S/D}\mspace{14mu} {ratio}}$?indicates text missing or illegible when filed

Where

FBG is Fasting Blood Glucose in mg/dl and normal value is 100 mg/dlTG is Triglycerides in mg/dl normal value is <150HBA1c is hemoglobin A1c in %, normal value is <6%BMI is body mass index as kg/m2 where a normal value is 20 and obesebegins above 25AST is Aspartate Transferase also called SGOT in IU/liter and a normalvalue is 5-50FB insulin is fasting Blood insulin concentration in nmol/liter, anormal value is 4.0

It should be noted that the 45 and 41 patients of the S/D ratiodescribed in Examples 1-3 were included as part of the FS Indexpopulation. The reason additional subgroups were added in the Example isto get beyond the use of HBA1c as a tool for diabetes alone, and thusmodel all the rest of the actions of metabolic syndrome. HBA1c aloneconsiders diabetes, which is one aspect of metabolic syndrome. On theother hand, the FS index considers all aspects of metabolic syndrome soit allows patients who have a multitude of Metabolic syndrome diseases,alone and in combination to be scored and monitored for changes causedby drug treatments. The FS index permits an assessment of the impact ofdrug therapy on the total metabolic syndrome profile of the patient atrisk for CV events.

Results:

The database included previously published 50 patients with T2D havingCV events principally myocardial infarctions, and controls of aprecisely matched group of 50 T2D patients without these events. Each ofthese patients had at least 5 years of data. FS index values werecalculated for these patients from serial laboratory and clinical dataover timeframes ranging 2-10 years. In these patient populations, anormal FS index value is 20-50, and values in this range are low CVrisk. Patients with two or more manifestations of Metabolic Syndrome areabove 200 and the highest values are above 500, values seen only whennearly every Metabolic Syndrome component is abnormal, as might beobserved in an extremely overweight T2D patient prior to RYGB surgery.It should be noted that RYGB can take the FS index of the aforementionedpatient to a normal value, providing evidence that each component ofmetabolic syndrome responds to stimulation of the ileal brake releasinghormones. This is a highly unexpected response and therefore newevidence for a novel mechanism for control of metabolic syndrome and itscomplicating cardiovascular risk

The outcomes of FS index calculations for the Myocardial Infarctionpatients vs their matched controls are shown in FIGS. 11 and 12.

FIG. 11. FS index values, BMI, HBA1c/SD ratio (HBSD) in 50 T2D patientswith cardiovascular events (winwCombevent_heart_ml_ip) over 5 years ofmonitoring

Here and In the graphics that follow (FIGS. 12-18), both FS index andHBA1c to SD ratio (HBSD) are displayed as output parameters over time,along with CV risk which is denoted by output parameter shown on thegraph as (+) and defined in the legend as winwCombevent_heart_MI_ip.

High FS index values preceded and therefore predicted CV events in thismetabolic syndrome patient population of patients, regardless of thespecific components of Metabolic Syndrome that were abnormal. Abnormaland rising FS index values predicted AMI although did not predict thetime of the event. A rapid rise in the FS index over 3-6 months was agood predictor of impending CV events. When Metabolic Syndrome isstudied as the equal weight of its components using the FS index, it isapparent why clinical strategies treating only one component ofMetabolic Syndrome do not predict or remove all risk of CV events.

FIG. 12. Patients with diabetes, but who do not have CV events differ inFS index values overall from patients with diabetes that do have CVevents.

Abnormal FS index values subsequently normalized, indicated resolutionof each component of Metabolic Syndrome, raising the possibility thatspecific treatments of Metabolic Syndrome might halt progression orreverse Metabolic Syndrome entirely.

The index also at least and partially explains why drug therapies thatimprove one aspect of Metabolic Syndrome, but worsen others, may notmitigate CV risk or remove CV events in complex Metabolic Syndromepatients. The index does also show that combination therapies consistingof individual drugs, each used for one component of metabolic syndromemy lower FS index by altering each component. One advantage of using theFS index is its perspective on the importance of combination therapy andin these specific examples to follow the FS index shows the importanceof certain combination therapy beneficial on the glucose supply side,such as RYGB surgery and the oral RYGB mimetic Brake.

Specific Example B

Cumulative CV risk signal from FS index elements; illustration ofpatients with MI across the entire modeling population of 250 patients

FIG. 13 displays the rank ordered CV output signal over our combinedpatient populations that comprise this model. There are over 250patients in this analysis, and in FIG. 13 the patients are arranged bylowest risk near the y axis intercept to highest risk at the oppositeend of the graphic display. Patients with myocardial infarctions aremarked with plus (+) signs in all cases, and it is clear that themajority of these MI patients are found in patients with FS indexassigned high baseline risk for CV events. This indicates that theneural net model predicts cardiovascular risk across all the diversepatients in this data analysis, itself a new finding because heretoforethere has been no laboratory test predictive of the risk ofcardiovascular events when many risk factors are present.

Results of the neural net model are illustrated over time for eachsubset of patients studied.

Specific Example C

Illustration of Metformin response and CV events in 61 patients with T2Dtreated with Metformin.

FIG. 14 illustrates our use of the neural net model applied to a T2Dpopulation of 61 patients initially treated with metformin alone, and acalculation of parameters such as FS index, HBA1c/SD ratio, and acalculated cumulative CV risk. Clearly, CV risk is relatively low withmetformin, but the T2D slowly progresses and as shown in FIG. 14 thepatients all slowly worsen. Their FS index rises, their HBSD rises, theygain weight and there is a progressive increase in FS index indicating aslow progression of CV risk. The progressive loss of diabetic controlleads to a quantified need for added therapy to the metformin regimen.In extreme cases the best approach may be to apply RYGB surgery in aneffort to control the underlying metabolic syndrome of the patient whois rapidly gaining weight with their T2D. Thus the FS index can point toa time to add anti-diabetic drugs beneficial to the treatment of theunderlying metabolic syndrome. In one particularly beneficial example,patients with rising FS index on metformin alone can be given Braketherapy, and when this combination is used their FS index will becomenormal. We have used this combination treatment approach guided by FSindex and have shown its benefit, an observation that justifies initialuse of Metformin combined with Brake early in diabetes as metforminalone does not control the T2D or the underlying metabolic syndrome.

As shown in FIG. 14, the usual pattern of FS index is flat or slowlyrising in patients given metformin alone. This indicates that metforminis not a treatment alone for metabolic syndrome.

Specific Example D

Illustration of Model output for RYGB surgery in 100 patients withsurgery to control obesity alone or concomitantly with T2D

In contrast to metformin and most other approaches to T2D where there islittle change in FS index, RYGB surgery greatly improves FS index asillustrated in FIG. 15. Furthermore, FS index improves rapidly asmetabolic syndrome improves and glucose supply declines. FIG. 15 showsthis improvement in 100 RYGB surgery patients, with almost a completelowering of CV risk to normal by 6-12 months after the RYGB surgeryprocedure. Many of said patients remain on metformin but they areusually no longer requiring insulin for their T2D.

Specific Example E

Modeling Results for 18 patients treated with Brake for Obesity,Hyperlipidemia, T2D or NAFLD, with illustrated method for study of thesepatients in real time.

In FIG. 16, the improvement in FS index and other parameters ofmetabolic syndrome is shown for 18 patients treated with Brake

The results of treatment displayed in FIG. 16 show that there is aboutthe same lowering of FS index from Brake as RYGB, an observation that ispredictive of both of these interventions lowering CV risk in patients.

The data from these patients shows a broad spectrum effect of Braketherapy, in that Brake controls the patient's hyperglycemia and HBA1c,controls elevated Triglycerides, controls elevated blood pressure,controls NAFLD and lowers all of the elevated hepatic enzymes, andrapidly lowers FB insulin and glucose, thus lowering insulin resistance.The combination of all of these effects from Brake is predicted by FSindex and in fact results in a normal FS index value in each patient by3-4 months of therapy.

In this manner an expanded benefit can be defined for either RYGB or theoral mimetic of RYGB called Brake.

Specific Example F

Modeling Results for 33 patients treated with exenatide (Byetta) forT2D, with illustrated method for treatment of these patients.

In FIG. 17, we employed FS index calculations to define the response toexenatide, trade-named Byetta. As shown in FIG. 17, the response to thisincretin hormone drug (a form of GLP-1 therapy) was a progressivelowering of FS index and a small change in BMI and HBSD.

The FS index calculation results displayed in FIG. 17 indicatebeneficial response to the diabetes component of metabolic syndrome,although there is not a major impact of Byetta on any other component ofmetabolic syndrome beyond obesity. Specifically, lipid abnormalities donot resolve and NAFLD abnormalities do not resolve. None of thesebiomarker responses of FS index components are linked specifically toGLP-1 responses alone, so this narrow band of effect of Byetta alone ononly the glucose supply side of T2D is typically dealt with by addingadditional pharmaceutical treatments to patients in need. Commonadditions to Byetta include a statin drug for lipid control, Omega3products for lowering of Triglycerides, an ACE inhibitor to controlblood pressure. There is no FDA approved treatment for NAFLD. Thus Brakeor RYGB have roles in the treatment of NAFLD. However, the conditionremains untreated in most patients given typical treatments for type 2diabetes. Affecting these laboratory biomarkers would be reflected inthe FS index as a beneficial lowering of CV risk, and would show benefitof adding Brake as a lowering of CV risk overall.

Patients given Byetta for treatment of T2D may be given oral Brake toprovide control of NAFLD, elevated triglycerides, blood pressure,insulin resistance, and weight. It should be evident to those skilled inthe art that any other GLP-1 injectable drugs employed in patients withT2D would also benefit from adding Brake therapy, specific exampleswould be stated to include but not be limited to Liraglutide (Victoza).

Specific Example G

Modeling Results for 12 patients treated with Sitagliptin (Januvia) forT2D, with illustrated method for treatment of these patients.

FIG. 18 illustrates the FS index response to treatment with sitagliptin,aka Januvia, in 12 T2D patients treated therewith and with the data usedto analyzed changes in FS index produced by the treatment.

The FS index calculation results displayed in FIG. 18 indicatebeneficial response to the diabetes component of metabolic syndrome,although there is not a major impact of Sitagliptin (Januvia) on anyother component of metabolic syndrome beyond hyperglycemia.Specifically, weight slowly increases or stays the same, lipidabnormalities do not resolve and NAFLD abnormalities do not resolve.None of these biomarker responses of FS index components are linkedspecifically to GLP-1 responses alone, so this narrow band of effect ofJanuvia alone on only the glucose supply side of T2D is typically dealtwith by adding additional pharmaceutical treatments to patients in need.Common additions to Januvia include a statin drug for lipid control,Omega3 products for lowering of Triglycerides, an ACE inhibitor tocontrol blood pressure. There is no FDA approved treatment for NAFLD.The other treatments do not affect the condition with the exception ofBrake or RYGB, so this part of the condition remains untreated in mostpatients.

We have treated Patients given Januvia and similar drugs for treatmentof T2D at the point where there is stable response but not resolution ofT2D, adding oral Brake to the Januvia or other DPP_IV inhibitor toprovide control of HBA1c, NAFLD, elevated triglycerides, blood pressure,insulin resistance, and weight. Affecting these laboratory biomarkerswould be reflected in the FS index as a beneficial lowering of CV risk,and would show benefit of adding Brake as a lowering of CV risk overall.In at least one of these patients additional decline in HBA1c was shownand the FS index was normal during treatment. It should be evident tothose skilled in the art that any other DPP-IV inhibitor employed inpatients with T2D would also benefit from adding Brake therapy, specificexamples would be stated to include but not be limited to saxagliptin(onglyza) Linagliptin (Tradgenta) alogliptin (if approved by FDA) andothers.

Specific Example H The Use of FS Index in Selection of Patients in Needof Treatment for Metabolic Syndrome and a Means of Defining the Responseto Treatment of Said Metabolic Syndrome

The final model for implementing this metabolic syndrome CV progressionmodel is an application for individual patients on a computer such as aweb-enabled cellphone, an I-pad or a Windows 8 tablet. The applicationwill record weight, food intake, calories from specific type of food,and exercise. From these, each patient's insulin output and CV risk iscalculated daily and the metabolic syndrome progression is linked tofood and lifestyle. Once the links are established for each patient, theapplication puts the patient onto an optimization plan that shouldminimize disease and maximize life expectancy. An example of a weightreduction tracked on said application for one patient is FIG. 19

Weight is plotted in FIG. 19 as pounds decreased from baseline over atime of 80 days when monitored using said I-pad application. Thissubject, a 55 year old female, was on a weight reduction program onlyand did not have abnormalities beyond a mild form of dietary associatedmetabolic syndrome.

Discussion of Modeling Results

Overall, the FS(Fayad/Schentag) index, which is composed of mostlyreadily available laboratory and clinical measures, appears to be aneffective means of describing progression or amelioration of the endorgan manifestations of metabolic syndromes in routine practice,including the changes that occur as a result of organ or systemregeneration after RYGB surgery or treatment with Brake. Its use inaggregate or use of its principle components separately are herebydesignated as a primary means of demonstrating direction of metabolicsyndrome manifestations (improved or worsening) and the impact oftherapeutic interventions designed to improve metabolic syndrome viastop and repair mechanisms of action. To avoid doubt, said therapeuticinterventions include both RYGB and combinations of pharmaceuticalswherein the composition of said pharmaceuticals includes Brake or itsspecific components in a dosage range between 7,500 mg and 20,000 mg.

Clinical proof of the utility of the synergistic combination of thesetherapies for other metabolic syndrome diseases, such as Alzheimer'sdisease, would necessitate the adoption of biomarkers of metabolicsyndrome progression such as the FS index, which is an overall biomarkerprofile that can point to regenerative processes that respond to RYGB orBrake. Added to the metabolic syndrome biomarker profile of the FS indexwould be a biomarker profile of Alzheimer's disease progression. Thislatter progression profile would focus on cognition, genomics whereapplicable, and imaging where applicable to loss of brain tissue andneuronal mass. To the extent that these biomarkers are improved bydonepezil, those effects carry forward. To the extent that the observedimprovement is tied to effects beyond those of donepezil, the conclusionwould be Brake associated recovery or regeneration of functioningneurons.

References for Example 4

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We claim:
 1. A method for determining cardiovascular risk for an individual suspected of having, at risk for, or diagnosed with a Metabolic Syndrome, the method comprising: a) obtaining from the individual one or more biological parameters, and from the biological parameters: b) determining a Fayad/Schentag (FS) index, wherein the FS index is calculated as: $\mspace{79mu} \frac{\begin{matrix} {{0.11\left( {{FBG} + {TG}} \right)} + {{HBA}\; 1c \times \frac{\text{?}}{4}} + {{BMI} \times \frac{\text{?}}{\text{?}}} +} \\ {{{AST} \times \frac{\text{?}}{100}} + {{FB}\mspace{14mu} {insulin}\; \times \left( {{SMI} - 22} \right)}} \end{matrix}}{{S/D}\mspace{14mu} {ratio}}$ ?indicates text missing or illegible when filed wherein the FBG is Fasting Blood Glucose in mg/dl; the TG is Triglycerides in mg/dl; the HBA1c is hemoglobin A1c in %; the BMI is body mass index in kg/m²; AST is Aspartate Transferase in IU/liter; FB insulin is fasting Blood insulin concentration in nmol/liter; and the S/D ratio (SD) is a ratio of Glucose Supply Index (S) to Insulin Demand Index (D) calculated as follows: 1+[aggregate of carbohydrate exposure(CE)+hepatic glucose uptake(HGU)+hepatic gluconeogenesis(GNG)and+insulin resistance(IR)], and (D) calculated as follows: 1+[aggregate of peripheral glucose uptake(PGU)+peripheral insulin exposure(PIE)]; c) assign the FS index determined in b) to the individual, wherein an FS index value of greater than 60 is indicative that the individual is in need of therapy for Metabolic Syndrome or at risk for at least one cardiovascular complication associated with Metabolic Syndrome.
 2. The method of claim 1, wherein step b) comprises determining an FS index value of greater than 60, the method further comprising administering to the individual a drug, wherein the drug is used to treat or resolve the Metabolic Syndrome and is pH-encapsulated glucose, wherein the pH encapsulated glucose is released in the intestine in the jejunum or ileum of said individual wherein the conditions for release of pH encapsulated glucose are a pH reading at or above a pH of 7.0.
 3. The method of claim 1, wherein the individual has been diagnosed with Metabolic Syndrome, wherein the individual is being treated for Metabolic Syndrome with at least one drug known to be active against at least one component of the FS index, and wherein the FS index value obtained in step b) is a first FS value, the method further comprising repeating steps a), b) and c) after a period of time during which the individual continues treatment with the at least one drug to provide a second FS index value, wherein a lower second FS value relative to the first FS index value indicates the at least one drug is effective for treating the Metabolic Syndrome in the individual, and wherein a higher second FS value relative to the first FS index value indicates that the individual is in need of a change in dosing of the first drug, or a change to a different drug, or is a candidate for bariatric surgery.
 4. The method of claim 3, wherein the at least one drug with which the individual is being treated is not a pH-encapsulated glucose drug, the method comprising determining a higher second FS index value relative to the first FS index value, and administering to the individual pH-encapsulated glucose drug, wherein the glucose is released at or above a pH of 7.0.
 5. The method of claim 2, wherein at the time the determining the FS index value of greater than 60 is performed, the individual is not being treated with a Type I diabetes drug, and wherein the SD value is
 1. 6. The method of claim 2, wherein the FS value is determined using a microprocessor.
 8. The method of claim 2, wherein the SD ratio is determined using a programmable spreadsheet.
 9. The method of claim 2, wherein severity of one or more of the cardiovascular complications associated with Metabolic Syndrome is reduced subsequent to the administration of the pH-encapsulated glucose.
 10. The method of claim 2, wherein the pH encapsulated glucose is administered in combination with an additional agent selected from the group consisting of anti-diabetes drugs, insulin, statin drugs, hormones, GLP-1 drugs, lipids, proteins, amino-acids, other sugars or carbohydrates, Metformin, Sitagliptin, and combinations thereof.
 11. The method of claim 9, wherein the pH-encapsulated glucose is administered in a dosage of from 5 grams to 20 grams of the pH-encapsulated glucose. 